pgSlam/pgSlam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "\n",
    "A robot is travelling in a oval trajectory. It is equipped with wheel odometry for odometry information and RGBD sensors for loop closure and ICP transformation. Due to noise in wheel odometry it generated a noisy estimate of the trajectory. Our task is to use loop closure pairs to correct the drift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from gtsam import *\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import numpy as np\n",
    "from gtsam.utils import plot as gtsam_plot\n",
    "np.random.seed(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation Utility\n",
    "Drawing functions for plotting one, two and three trajectories simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(X, Y, THETA):\n",
    "    ax = plt.subplot(111)\n",
    "    ax.plot(X, Y, 'ro')\n",
    "    plt.plot(X, Y, 'k-')\n",
    "\n",
    "    for i in range(len(THETA)):\n",
    "        x2 = 0.25*math.cos(THETA[i]) + X[i]\n",
    "        y2 = 0.25*math.sin(THETA[i]) + Y[i]\n",
    "        plt.plot([X[i], x2], [Y[i], y2], 'm->')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def drawTwo(X1, Y1, THETA1, X2, Y2, THETA2):\n",
    "    ax = plt.subplot(111)\n",
    "    ax.plot(X1, Y1, 'ro', label='Ground Truth')\n",
    "    plt.plot(X1, Y1, 'k-')\n",
    "\n",
    "    for i in range(len(THETA1)):\n",
    "        x2 = 0.25*math.cos(THETA1[i]) + X1[i]\n",
    "        y2 = 0.25*math.sin(THETA1[i]) + Y1[i]\n",
    "        plt.plot([X1[i], x2], [Y1[i], y2], 'r->')\n",
    "\n",
    "    ax.plot(X2, Y2, 'bo', label='Optimized')\n",
    "    plt.plot(X2, Y2, 'k-')\n",
    "\n",
    "    for i in range(len(THETA2)):\n",
    "        x2 = 0.25*math.cos(THETA2[i]) + X2[i]\n",
    "        y2 = 0.25*math.sin(THETA2[i]) + Y2[i]\n",
    "        plt.plot([X2[i], x2], [Y2[i], y2], 'b->')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def drawThree(X1, Y1, THETA1, X2, Y2, THETA2, X3, Y3, THETA3):\n",
    "    ax = plt.subplot(111)\n",
    "    ax.plot(X1, Y1, 'ro', label='Ground Truth')\n",
    "    plt.plot(X1, Y1, 'k-')\n",
    "\n",
    "    for i in range(len(THETA1)):\n",
    "        x2 = 0.25*math.cos(THETA1[i]) + X1[i]\n",
    "        y2 = 0.25*math.sin(THETA1[i]) + Y1[i]\n",
    "        plt.plot([X1[i], x2], [Y1[i], y2], 'r->')\n",
    "\n",
    "    ax.plot(X2, Y2, 'bo', label='Optimized')\n",
    "    plt.plot(X2, Y2, 'k-')\n",
    "\n",
    "    for i in range(len(THETA2)):\n",
    "        x2 = 0.25*math.cos(THETA2[i]) + X2[i]\n",
    "        y2 = 0.25*math.sin(THETA2[i]) + Y2[i]\n",
    "        plt.plot([X2[i], x2], [Y2[i], y2], 'b->')\n",
    "\n",
    "    ax.plot(X3, Y3, 'go', label='Noisy')\n",
    "    plt.plot(X3, Y3, 'k-')\n",
    "\n",
    "    for i in range(len(THETA3)):\n",
    "        x2 = 0.25*math.cos(THETA3[i]) + X3[i]\n",
    "        y2 = 0.25*math.sin(THETA3[i]) + Y3[i]\n",
    "        plt.plot([X3[i], x2], [Y3[i], y2], 'g->')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()\t\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating X, Y coordinates and $\\theta$ separately for the trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to generate the trajectory\n",
    "def genTraj():\n",
    "    init = (0, 0)\n",
    "\n",
    "    # Forward I\n",
    "    num = 20; xSt = -5; ySt = -8; leng = 9.0; step = float(leng)/num\n",
    "    X1 = np.zeros(num); Y1 = np.zeros(num); X1[0] = xSt; Y1[0] = ySt\n",
    "    for i in range(1, num):\n",
    "        X1[i] = X1[i-1] + step\n",
    "        Y1[i] = ySt\n",
    "\n",
    "    # UTurn I\n",
    "    rad = 2.5; num = 20\n",
    "    xCen = X1[-1]; yCen = Y1[-1] + rad\n",
    "    thetas = np.linspace(-math.pi/2, math.pi/2, num)\n",
    "    X2 = np.zeros(num); Y2 = np.zeros(num)\n",
    "    for i, theta in enumerate(thetas):\n",
    "        X2[i] = (xCen + rad*math.cos(theta))\n",
    "        Y2[i] = (yCen + rad*math.sin(theta))\n",
    "\n",
    "    # Backward I\n",
    "    num = 20; leng = 10.0; step = float(leng)/num\n",
    "    xSt = X2[-1]; ySt = Y2[-1]\n",
    "    X3 = np.zeros(num); Y3 = np.zeros(num); X3[0] = xSt; Y3[0] = ySt \n",
    "    for i in range(1, num):\n",
    "        X3[i] = X3[i-1] - step\n",
    "        Y3[i] = ySt\n",
    "\n",
    "    # UTurn II\n",
    "    rad = 2.6; num = 20\n",
    "    xCen = X3[-1]; yCen = Y3[-1] - rad\n",
    "    thetas = np.linspace(math.pi/2, 3*math.pi/2, num)\n",
    "    X4 = np.zeros(num); Y4 = np.zeros(num)\n",
    "    for i, theta in enumerate(thetas):\n",
    "        X4[i] = (xCen + rad*math.cos(theta))\n",
    "        Y4[i] = (yCen + rad*math.sin(theta))\n",
    "\n",
    "    # Forward II\n",
    "    num = 20; leng = 11.0; step = float(leng)/num\n",
    "    xSt = X4[-1]; ySt = Y4[-1]\n",
    "    X5 = np.zeros(num); Y5 = np.zeros(num); X5[0] = xSt; Y5[0] = ySt\n",
    "    for i in range(1, num):\n",
    "        X5[i] = X5[i-1] + step\n",
    "        Y5[i] = ySt\n",
    "\n",
    "    # UTurn III\n",
    "    rad = 2.7; num = 20\n",
    "    xCen = X5[-1]; yCen = Y5[-1] + rad\n",
    "    thetas = np.linspace(-math.pi/2, math.pi/2, num)\n",
    "    X6 = np.zeros(num); Y6 = np.zeros(num)\n",
    "    for i, theta in enumerate(thetas):\n",
    "        X6[i] = (xCen + rad*math.cos(theta))\n",
    "        Y6[i] = (yCen + rad*math.sin(theta))\t\n",
    "\n",
    "    # Assemble\n",
    "    X = np.concatenate([X1, X2, X3, X4, X5, X6]); Y = np.concatenate([Y1, Y2, Y3, Y4, Y5, Y6])\n",
    "    THETA = np.array(getTheta(X, Y))\n",
    "\n",
    "    return (X, Y, THETA)\n",
    "\n",
    "# function to get theta by taking the slope of the line joining the previous and next point\n",
    "def getTheta(X ,Y):\n",
    "    THETA = [None]*len(X)\n",
    "    for i in range(1, len(X)-1):\n",
    "        if(X[i+1] == X[i-1]):\n",
    "            if (Y[i+1]>Y[i-1]):\n",
    "                THETA[i] = math.pi/2\n",
    "            else:\n",
    "                THETA[i] = 3*math.pi/2\n",
    "            continue\n",
    "\n",
    "        THETA[i] = math.atan((Y[i+1]-Y[i-1])/(X[i+1]-X[i-1]))\n",
    "\n",
    "        if(X[i+1]-X[i-1] < 0):\n",
    "            THETA[i] += math.pi \n",
    "\n",
    "    if X[1]==X[0]:\n",
    "        if Y[1] > Y[0]:\n",
    "            THETA[0] = math.pi/2\n",
    "        else:\n",
    "            THETA[0] = 3*math.pi/2\n",
    "    else:\n",
    "        THETA[0] = math.atan((Y[1]-Y[0])/(X[1]-X[0]))\n",
    "\n",
    "    if X[-1] == X[len(Y)-2]:\n",
    "        if Y[1] > Y[0]:\n",
    "            THETA[-1] = math.pi/2\n",
    "        else:\n",
    "            THETA[-1] = 3*math.pi/2\n",
    "    else:\n",
    "        THETA[-1] = math.atan((Y[-1]-Y[len(Y)-2])/(X[-1]-X[len(Y)-2]))\n",
    "\n",
    "    return THETA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ground Truth Trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(X, Y, THETA) = genTraj()\n",
    "draw(X, Y, THETA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding noise to the trajectory to simulate real life scenario.  \n",
    "We could not directly add noise values to $X, Y, \\theta$ parameters. Instead noise should be added to the odometry values.  \n",
    "T1_w = $T^w_1$ = 1 with respect to the world"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addNoise(X, Y, THETA):\n",
    "    xN = np.zeros(len(X)); yN = np.zeros(len(Y)); tN = np.zeros(len(THETA))\n",
    "    xN[0] = X[0]; yN[0] = Y[0]; tN[0] = THETA[0]\n",
    "\n",
    "    for i in range(1, len(X)):\n",
    "        # Get T2_1\n",
    "        p1 = (X[i-1], Y[i-1], THETA[i-1])\n",
    "        p2 = (X[i], Y[i], THETA[i])\n",
    "        T1_w = np.array([[math.cos(p1[2]), -math.sin(p1[2]), p1[0]], [math.sin(p1[2]), math.cos(p1[2]), p1[1]], [0, 0, 1]])\n",
    "        T2_w = np.array([[math.cos(p2[2]), -math.sin(p2[2]), p2[0]], [math.sin(p2[2]), math.cos(p2[2]), p2[1]], [0, 0, 1]])\n",
    "        T2_1 = np.dot(np.linalg.inv(T1_w), T2_w)\n",
    "        del_x = T2_1[0][2]\n",
    "        del_y = T2_1[1][2]\n",
    "        del_theta = math.atan2(T2_1[1, 0], T2_1[0, 0])\n",
    "\n",
    "        # Add noise\n",
    "        if(i<5):\n",
    "            xNoise = 0; yNoise = 0; tNoise = 0\n",
    "        else:\n",
    "            xNoise = np.random.normal(0, 0.03); yNoise = np.random.normal(0, 0.03); tNoise = np.random.normal(0, 0.03)\n",
    "        del_xN = del_x + xNoise; del_yN = del_y + yNoise; del_thetaN = del_theta + tNoise\n",
    "\n",
    "        # Convert to T2_1'\n",
    "        T2_1N = np.array([[math.cos(del_thetaN), -math.sin(del_thetaN), del_xN], [math.sin(del_thetaN), math.cos(del_thetaN), del_yN], [0, 0, 1]])\n",
    "\n",
    "        # Get T2_w' = T1_w' . T2_1'\n",
    "        p1 = (xN[i-1], yN[i-1], tN[i-1])\n",
    "        T1_wN = np.array([[math.cos(p1[2]), -math.sin(p1[2]), p1[0]], [math.sin(p1[2]), math.cos(p1[2]), p1[1]], [0, 0, 1]])\n",
    "        T2_wN = np.dot(T1_wN, T2_1N)\n",
    "\n",
    "        # Get x2', y2', theta2'\n",
    "        x2N = T2_wN[0][2]\n",
    "        y2N = T2_wN[1][2]\n",
    "        theta2N = math.atan2(T2_wN[1, 0], T2_wN[0, 0])\n",
    "\n",
    "        xN[i] = x2N; yN[i] = y2N; tN[i] = theta2N  \n",
    "\n",
    "    # tN = getTheta(xN, yN)\n",
    "\n",
    "    return (xN, yN, tN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(xN, yN, tN) = addNoise(X, Y, THETA)\n",
    "draw(xN, yN, tN)"
   ]
  },
  {
   "attachments": {
    "loop_clousre_noisy.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating G2O file. We would be using `VERTEX_SE2` and `EDGE_SE2` for our task.  \n",
    "Information matrix for odometry edges, $\\omega_{odom}$ = `500.0 0.0 0.0 500.0 0.0 500.0`   \n",
    "Information matrix for loop closure edges, $\\omega_{lc}$ = `700.0 0.0 0.0 700.0 0.0 700.0`  \n",
    "Loop closure constraints are generated for the starting part of the trajectory. In practice, they would be detected by loop closure system using bag of visual words and transformation would be calculated using ICP.  \n",
    "Fix the starting node in pose graph otherwise the optimized pose graph can occur anywhere on cartesian plane.  \n",
    "Below figure is generated using `g2o_viewer`, in terminal after generating `noise.g2o`, run: `g2o_viewer noise.g2o`  \n",
    "![loop_clousre_noisy.png](attachment:loop_clousre_noisy.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = NonlinearFactorGraph()\n",
    "initial = Values()\n",
    "\n",
    "def writeOdomToGtsam(X, Y, THETA, graph, values):\n",
    "    graph.add(PriorFactorPose2(0, Pose2(X[0], Y[0], THETA[0]), noiseModel.Diagonal.Sigmas(np.array([0.000000001, 0.0000000001, 0.000000000001]))))\n",
    "    values.insert(0, Pose2(X[0], Y[0], THETA[0]))\n",
    "    pose = Pose2(X[0], Y[0], THETA[0])\n",
    "    for i, (x, y, theta) in enumerate(zip(X,Y,THETA)):\n",
    "        if i > 0:\n",
    "            pose_prev = pose\n",
    "            pose = Pose2(x, y, theta)\n",
    "            graph.add(BetweenFactorPose2(i-1, i, pose_prev.inverse()*pose, noiseModel.Diagonal.Sigmas(np.array([0.0044, 0.0044, 0.0044]))))\n",
    "            values.insert(i, Pose2(x, y, theta))\n",
    "        # print(i, i+1, x, y, theta)\n",
    "\n",
    "def writeLooptoGtsam(X, Y, THETA, graph, values):\n",
    "    s2 = X.size\n",
    "    pairs = []\n",
    "    for i in range(0, 40, 2):\n",
    "        pairs.append((i, i+80))\n",
    "    \n",
    "    for p in pairs:\n",
    "        p1 = (X[p[0]], Y[p[0]], THETA[p[0]])\n",
    "        p2 = (X[p[1]], Y[p[1]], THETA[p[1]])\n",
    "        T1_w = np.array([[math.cos(p1[2]), -math.sin(p1[2]), p1[0]], [math.sin(p1[2]), math.cos(p1[2]), p1[1]], [0, 0, 1]])\n",
    "        T2_w = np.array([[math.cos(p2[2]), -math.sin(p2[2]), p2[0]], [math.sin(p2[2]), math.cos(p2[2]), p2[1]], [0, 0, 1]])\n",
    "        T2_1 = np.dot(np.linalg.inv(T1_w), T2_w)\n",
    "        del_x = T2_1[0][2]\n",
    "        del_y = T2_1[1][2]\n",
    "        del_theta = math.atan2(T2_1[1, 0], T2_1[0, 0])\n",
    "\n",
    "        graph.add(BetweenFactorPose2(p[0], p[1], Pose2(del_x, del_y, del_theta), noiseModel.Diagonal.Sigmas(np.array([0.0137, 0.0137, 0.0137]))))\n",
    "        # print(p[0], p[1], del_x, del_y, del_theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial error: 128148\n",
      "newError: 3251.64\n",
      "errorThreshold: 3251.64 > 0\n",
      "absoluteDecrease: 124896.729352 >= 1e-05\n",
      "relativeDecrease: 0.97462598579 >= 1e-05\n",
      "newError: 390.504656099\n",
      "errorThreshold: 390.504656099 > 0\n",
      "absoluteDecrease: 2861.13385094 >= 1e-05\n",
      "relativeDecrease: 0.879905267681 >= 1e-05\n",
      "newError: 386.541837999\n",
      "errorThreshold: 386.541837999 > 0\n",
      "absoluteDecrease: 3.96281809988 >= 1e-05\n",
      "relativeDecrease: 0.0101479407172 >= 1e-05\n",
      "newError: 386.531377004\n",
      "errorThreshold: 386.531377004 > 0\n",
      "absoluteDecrease: 0.0104609951775 >= 1e-05\n",
      "relativeDecrease: 2.7063034707e-05 >= 1e-05\n",
      "newError: 386.530888876\n",
      "errorThreshold: 386.530888876 > 0\n",
      "absoluteDecrease: 0.000488128037546 >= 1e-05\n",
      "relativeDecrease: 1.26284194916e-06 < 1e-05\n",
      "converged\n",
      "errorThreshold: 386.530888876 <? 0\n",
      "absoluteDecrease: 0.000488128037546 <? 1e-05\n",
      "relativeDecrease: 1.26284194916e-06 <? 1e-05\n",
      "iterations: 5 >? 100\n"
     ]
    }
   ],
   "source": [
    "writeOdomToGtsam(xN, yN, tN, graph=graph, values=initial)\n",
    "marginals_without_loop = Marginals(graph, initial)\n",
    "writeLooptoGtsam(X, Y, THETA, graph=graph, values=initial)\n",
    "\n",
    "#optimize\n",
    "from gtsam import LevenbergMarquardtOptimizer\n",
    "params = LevenbergMarquardtParams()\n",
    "params.setVerbosity('ERROR')\n",
    "optimizer = LevenbergMarquardtOptimizer(graph, initial, params)\n",
    "result = optimizer.optimize()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factor Graph:\n",
      "size: 140\n",
      "\n",
      "Factor 0: PriorFactor on 0\n",
      "  prior mean:  (-5, -8, 0)\n",
      "  noise model: constrained sigmas [1e-09; 1e-10; 1e-12];\n",
      "  noise model: constrained mu [1000; 1000; 1000];\n",
      "\n",
      "Factor 1: BetweenFactor(0,1)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 2: BetweenFactor(1,2)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 3: BetweenFactor(2,3)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 4: BetweenFactor(3,4)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 5: BetweenFactor(4,5)\n",
      "  measured:  (0.451516851, 0.01499854, -0.0298772679)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 6: BetweenFactor(5,6)\n",
      "  measured:  (0.470807955, -0.0125490456, -0.0475373171)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 7: BetweenFactor(6,7)\n",
      "  measured:  (0.430568797, 0.0179572552, 0.00996750098)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 8: BetweenFactor(7,8)\n",
      "  measured:  (0.415575701, 0.0185600907, -0.00263960785)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 9: BetweenFactor(8,9)\n",
      "  measured:  (0.462752172, 0.00996759436, -0.0347044878)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 10: BetweenFactor(9,10)\n",
      "  measured:  (0.460529915, -0.0182066185, 0.0464093799)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 11: BetweenFactor(10,11)\n",
      "  measured:  (0.471700248, 0.00138406702, -0.0294897496)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 12: BetweenFactor(11,12)\n",
      "  measured:  (0.451632982, 0.00479678805, -0.0362684448)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 13: BetweenFactor(12,13)\n",
      "  measured:  (0.516700807, 0.0118288564, 0.0507707315)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 14: BetweenFactor(13,14)\n",
      "  measured:  (0.416615635, 0.0490724263, -0.0408289678)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 15: BetweenFactor(14,15)\n",
      "  measured:  (0.430463225, 0.0162735393, 0.00144018742)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 16: BetweenFactor(15,16)\n",
      "  measured:  (0.379257791, -0.0331675213, 0.0251350906)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 17: BetweenFactor(16,17)\n",
      "  measured:  (0.512636126, 0.0274452287, -0.00828610063)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 18: BetweenFactor(17,18)\n",
      "  measured:  (0.473895357, -0.0343139572, 0.0152975935)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 19: BetweenFactor(18,19)\n",
      "  measured:  (0.409576191, -0.000280803019, -0.00392113916)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 20: BetweenFactor(19,20)\n",
      "  measured:  (0.0240625984, -0.00908891901, 0.118733569)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 21: BetweenFactor(20,21)\n",
      "  measured:  (0.406994369, 0.0250958611, 0.106271559)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 22: BetweenFactor(21,22)\n",
      "  measured:  (0.3562602, 0.0352231661, 0.166424823)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 23: BetweenFactor(22,23)\n",
      "  measured:  (0.388124278, 0.0394790629, 0.121680952)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 24: BetweenFactor(23,24)\n",
      "  measured:  (0.428172032, 0.0493901071, 0.174360348)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 25: BetweenFactor(24,25)\n",
      "  measured:  (0.485784001, 0.0446670434, 0.167371112)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 26: BetweenFactor(25,26)\n",
      "  measured:  (0.389518535, 0.0430109778, 0.136493678)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 27: BetweenFactor(26,27)\n",
      "  measured:  (0.449641034, 0.0146674055, 0.170101068)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 28: BetweenFactor(27,28)\n",
      "  measured:  (0.471188966, 0.0690223683, 0.172626787)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 29: BetweenFactor(28,29)\n",
      "  measured:  (0.452884079, 0.0324599803, 0.189204)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 30: BetweenFactor(29,30)\n",
      "  measured:  (0.412059175, 0.00693359739, 0.178255122)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 31: BetweenFactor(30,31)\n",
      "  measured:  (0.439525978, 0.0237136853, 0.132433325)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 32: BetweenFactor(31,32)\n",
      "  measured:  (0.395640594, -0.0372965167, 0.147116471)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 33: BetweenFactor(32,33)\n",
      "  measured:  (0.379227773, 0.0947688935, 0.148400723)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 34: BetweenFactor(33,34)\n",
      "  measured:  (0.365198604, 0.0602219949, 0.160090666)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 35: BetweenFactor(34,35)\n",
      "  measured:  (0.412944566, 0.0397561276, 0.171626386)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 36: BetweenFactor(35,36)\n",
      "  measured:  (0.400253128, 0.0627376994, 0.181044412)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 37: BetweenFactor(36,37)\n",
      "  measured:  (0.39661192, 0.0289528032, 0.137015924)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 38: BetweenFactor(37,38)\n",
      "  measured:  (0.419912416, 0.0562441548, 0.184869579)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 39: BetweenFactor(38,39)\n",
      "  measured:  (0.429928695, 0.0302996837, 0.130039718)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 40: BetweenFactor(39,40)\n",
      "  measured:  (-0.00621545032, 0.000902012452, 0.0725880957)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 41: BetweenFactor(40,41)\n",
      "  measured:  (0.502437913, -0.00721572532, 0.0609653798)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 42: BetweenFactor(41,42)\n",
      "  measured:  (0.512775401, -0.0504889467, -0.0130165914)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 43: BetweenFactor(42,43)\n",
      "  measured:  (0.457513174, 0.0263969699, 0.041237797)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 44: BetweenFactor(43,44)\n",
      "  measured:  (0.486468713, 0.0184059788, -0.00630469689)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 45: BetweenFactor(44,45)\n",
      "  measured:  (0.512241745, 0.0519719552, -0.0104834387)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 46: BetweenFactor(45,46)\n",
      "  measured:  (0.463005895, 0.0215482317, 0.015496789)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 47: BetweenFactor(46,47)\n",
      "  measured:  (0.520214045, 0.0307936491, -0.0204344529)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 48: BetweenFactor(47,48)\n",
      "  measured:  (0.441019558, 0.031227191, -0.0122595094)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 49: BetweenFactor(48,49)\n",
      "  measured:  (0.463777986, -0.0350487085, 0.0338511999)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 50: BetweenFactor(49,50)\n",
      "  measured:  (0.505424999, 0.00908706104, 0.0255116061)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 51: BetweenFactor(50,51)\n",
      "  measured:  (0.479692972, -0.0337534377, -0.0255885384)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 52: BetweenFactor(51,52)\n",
      "  measured:  (0.466862762, -0.00873287395, -0.026729272)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 53: BetweenFactor(52,53)\n",
      "  measured:  (0.501159056, -0.0144254743, -0.00239938467)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 54: BetweenFactor(53,54)\n",
      "  measured:  (0.51949371, 0.0420383068, 0.0466781375)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 55: BetweenFactor(54,55)\n",
      "  measured:  (0.503447665, 0.00637110862, -0.0152514069)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 56: BetweenFactor(55,56)\n",
      "  measured:  (0.53017946, 0.0459537259, -0.0178080541)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 57: BetweenFactor(56,57)\n",
      "  measured:  (0.52292317, -0.00988699617, -0.00513344985)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 58: BetweenFactor(57,58)\n",
      "  measured:  (0.505528543, -0.0120353045, 0.0268531313)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 59: BetweenFactor(58,59)\n",
      "  measured:  (0.455124429, -0.0208619073, -0.00358234324)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 60: BetweenFactor(59,60)\n",
      "  measured:  (-0.0313447111, 0.0108191395, 0.0372263619)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 61: BetweenFactor(60,61)\n",
      "  measured:  (0.431067924, -0.0432079463, 0.0783160888)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 62: BetweenFactor(61,62)\n",
      "  measured:  (0.443188134, 0.101567532, 0.197676012)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 63: BetweenFactor(62,63)\n",
      "  measured:  (0.476463171, 0.037499777, 0.228819057)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 64: BetweenFactor(63,64)\n",
      "  measured:  (0.420708175, 0.0278946859, 0.126884497)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 65: BetweenFactor(64,65)\n",
      "  measured:  (0.434613487, 0.0670686233, 0.146331237)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 66: BetweenFactor(65,66)\n",
      "  measured:  (0.416827869, 0.021554262, 0.155403405)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 67: BetweenFactor(66,67)\n",
      "  measured:  (0.410234433, -0.00504058057, 0.206776026)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 68: BetweenFactor(67,68)\n",
      "  measured:  (0.368793342, 0.00764814532, 0.175659225)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 69: BetweenFactor(68,69)\n",
      "  measured:  (0.458292787, 0.0292025867, 0.156849593)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 70: BetweenFactor(69,70)\n",
      "  measured:  (0.429430231, 0.0276212499, 0.191649256)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 71: BetweenFactor(70,71)\n",
      "  measured:  (0.413459574, 0.00589805732, 0.151167097)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 72: BetweenFactor(71,72)\n",
      "  measured:  (0.430833973, 0.072337013, 0.213343307)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 73: BetweenFactor(72,73)\n",
      "  measured:  (0.415963506, -0.00846489569, 0.123830993)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 74: BetweenFactor(73,74)\n",
      "  measured:  (0.491705599, 0.0233745177, 0.179084168)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 75: BetweenFactor(74,75)\n",
      "  measured:  (0.48546448, 0.0150571239, 0.159509762)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 76: BetweenFactor(75,76)\n",
      "  measured:  (0.41052808, 0.00258313105, 0.189339648)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 77: BetweenFactor(76,77)\n",
      "  measured:  (0.459916928, -0.020751542, 0.194798302)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 78: BetweenFactor(77,78)\n",
      "  measured:  (0.420866921, 0.0409694007, 0.171283052)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 79: BetweenFactor(78,79)\n",
      "  measured:  (0.388749775, 0.0261344837, 0.0529460943)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 80: BetweenFactor(79,80)\n",
      "  measured:  (0.0574575472, 0.0655754445, 0.0851944927)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 81: BetweenFactor(80,81)\n",
      "  measured:  (0.514631319, 0.0346816819, -0.00570005503)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 82: BetweenFactor(81,82)\n",
      "  measured:  (0.602363335, 0.0590615817, 0.0501871929)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 83: BetweenFactor(82,83)\n",
      "  measured:  (0.538142216, -0.0299422436, -0.0198915002)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 84: BetweenFactor(83,84)\n",
      "  measured:  (0.547109643, -0.02771422, 0.0358010553)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 85: BetweenFactor(84,85)\n",
      "  measured:  (0.569723925, -0.00101895855, -0.0120117896)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 86: BetweenFactor(85,86)\n",
      "  measured:  (0.569479434, 0.0413525866, -0.0578938603)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 87: BetweenFactor(86,87)\n",
      "  measured:  (0.557016208, -0.0090139592, -0.022326891)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 88: BetweenFactor(87,88)\n",
      "  measured:  (0.53976232, 0.00403551374, 0.0560869656)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 89: BetweenFactor(88,89)\n",
      "  measured:  (0.551711715, -0.0128930026, -0.0368157044)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 90: BetweenFactor(89,90)\n",
      "  measured:  (0.573219978, -0.0225561227, -0.00341235241)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 91: BetweenFactor(90,91)\n",
      "  measured:  (0.571392604, 0.00157111807, -0.0115868592)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 92: BetweenFactor(91,92)\n",
      "  measured:  (0.544818404, 0.0269504706, -0.000589086624)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 93: BetweenFactor(92,93)\n",
      "  measured:  (0.557280196, 0.012685454, -0.0100361806)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 94: BetweenFactor(93,94)\n",
      "  measured:  (0.559838173, -0.00830427475, 0.0340152997)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 95: BetweenFactor(94,95)\n",
      "  measured:  (0.565054308, -0.0519101382, -0.00622061814)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 96: BetweenFactor(95,96)\n",
      "  measured:  (0.583275237, -0.00638597711, -0.0126755227)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 97: BetweenFactor(96,97)\n",
      "  measured:  (0.579810513, -0.0324787853, 0.00195104681)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 98: BetweenFactor(97,98)\n",
      "  measured:  (0.567704895, 0.0255578288, -0.0310005051)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 99: BetweenFactor(98,99)\n",
      "  measured:  (0.512613637, 0.04483671, -0.0233526546)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 100: BetweenFactor(99,100)\n",
      "  measured:  (-0.00269653236, 0.0729367201, 0.0687160289)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 101: BetweenFactor(100,101)\n",
      "  measured:  (0.379866528, 0.0836041078, 0.113050871)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 102: BetweenFactor(101,102)\n",
      "  measured:  (0.455169119, 0.120138521, 0.163089009)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 103: BetweenFactor(102,103)\n",
      "  measured:  (0.48778338, 0.0371982802, 0.157077643)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 104: BetweenFactor(103,104)\n",
      "  measured:  (0.463380828, 0.0633561153, 0.203818003)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 105: BetweenFactor(104,105)\n",
      "  measured:  (0.480240882, 0.0218628591, 0.162924793)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 106: BetweenFactor(105,106)\n",
      "  measured:  (0.502541536, 0.0550895974, 0.168273152)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 107: BetweenFactor(106,107)\n",
      "  measured:  (0.484828267, 0.0788293048, 0.204046343)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 108: BetweenFactor(107,108)\n",
      "  measured:  (0.428019844, 0.0252808816, 0.187911013)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 109: BetweenFactor(108,109)\n",
      "  measured:  (0.480176345, 0.000972619038, 0.151696276)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 110: BetweenFactor(109,110)\n",
      "  measured:  (0.437514703, 0.0670418942, 0.132856902)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 111: BetweenFactor(110,111)\n",
      "  measured:  (0.525554297, 0.0395261026, 0.183949481)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 112: BetweenFactor(111,112)\n",
      "  measured:  (0.511301344, 0.0556996438, 0.188001228)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 113: BetweenFactor(112,113)\n",
      "  measured:  (0.439247864, 0.0510834023, 0.133862374)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 114: BetweenFactor(113,114)\n",
      "  measured:  (0.442319004, 0.0381481009, 0.135857853)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 115: BetweenFactor(114,115)\n",
      "  measured:  (0.474811896, 0.0506119342, 0.109758945)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 116: BetweenFactor(115,116)\n",
      "  measured:  (0.468316088, 0.00929224947, 0.183049647)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 117: BetweenFactor(116,117)\n",
      "  measured:  (0.420603031, 0.0263251848, 0.178171154)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 118: BetweenFactor(117,118)\n",
      "  measured:  (0.502844, -0.00824354265, 0.155234127)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 119: BetweenFactor(118,119)\n",
      "  measured:  (0.434442067, 0.0698983734, -3.06599091)\n",
      "isotropic dim=3 sigma=0.0044\n",
      "\n",
      "Factor 120: BetweenFactor(0,80)\n",
      "  measured:  (-0.95, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 121: BetweenFactor(2,82)\n",
      "  measured:  (-0.75, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 122: BetweenFactor(4,84)\n",
      "  measured:  (-0.55, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 123: BetweenFactor(6,86)\n",
      "  measured:  (-0.35, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 124: BetweenFactor(8,88)\n",
      "  measured:  (-0.15, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 125: BetweenFactor(10,90)\n",
      "  measured:  (0.05, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 126: BetweenFactor(12,92)\n",
      "  measured:  (0.25, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 127: BetweenFactor(14,94)\n",
      "  measured:  (0.45, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 128: BetweenFactor(16,96)\n",
      "  measured:  (0.65, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 129: BetweenFactor(18,98)\n",
      "  measured:  (0.85, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 130: BetweenFactor(20,100)\n",
      "  measured:  (0.930239399, -0.277767277, 2.09265282e-16)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 131: BetweenFactor(22,102)\n",
      "  measured:  (0.89852638, -0.508464496, 6.0255218e-16)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 132: BetweenFactor(24,104)\n",
      "  measured:  (0.749683484, -0.783502077, 4.19513523e-16)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 133: BetweenFactor(26,106)\n",
      "  measured:  (0.51960075, -0.995308154, -8.00473149e-16)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 134: BetweenFactor(28,108)\n",
      "  measured:  (0.233211213, -1.12093025, 2.11891615e-17)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 135: BetweenFactor(30,110)\n",
      "  measured:  (-0.0784503782, -1.14675527, -2.20725686e-16)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 136: BetweenFactor(32,112)\n",
      "  measured:  (-0.381610653, -1.06998466, -7.22614353e-18)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 137: BetweenFactor(34,114)\n",
      "  measured:  (-0.643417493, -0.898937715, -2.0358705e-18)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 138: BetweenFactor(36,116)\n",
      "  measured:  (-0.835500064, -0.652150023, 4.17775779e-16)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n",
      "Factor 139: BetweenFactor(38,118)\n",
      "  measured:  (-0.937043238, -0.356364861, -5.09204256e-18)\n",
      "isotropic dim=3 sigma=0.0137\n",
      "\n"
     ]
    }
   ],
   "source": [
    "graph.print(\"Factor Graph:\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Estimate:\n",
      "\n",
      "Values with 120 values:\n",
      "Value 0: (gtsam::Pose2)\n",
      "(-5, -8, 0)\n",
      "\n",
      "Value 1: (gtsam::Pose2)\n",
      "(-4.55, -8, 0)\n",
      "\n",
      "Value 2: (gtsam::Pose2)\n",
      "(-4.1, -8, 0)\n",
      "\n",
      "Value 3: (gtsam::Pose2)\n",
      "(-3.65, -8, 0)\n",
      "\n",
      "Value 4: (gtsam::Pose2)\n",
      "(-3.2, -8, 0)\n",
      "\n",
      "Value 5: (gtsam::Pose2)\n",
      "(-2.74848315, -7.98500146, -0.0298772679)\n",
      "\n",
      "Value 6: (gtsam::Pose2)\n",
      "(-2.27826019, -8.01160927, -0.077414585)\n",
      "\n",
      "Value 7: (gtsam::Pose2)\n",
      "(-1.84759218, -8.02700482, -0.067447084)\n",
      "\n",
      "Value 8: (gtsam::Pose2)\n",
      "(-1.4317105, -8.03649505, -0.0700866919)\n",
      "\n",
      "Value 9: (gtsam::Pose2)\n",
      "(-0.969396391, -8.05895815, -0.10479118)\n",
      "\n",
      "Value 10: (gtsam::Pose2)\n",
      "(-0.51329715, -8.12523609, -0.0583817998)\n",
      "\n",
      "Value 11: (gtsam::Pose2)\n",
      "(-0.0423197944, -8.15137745, -0.0878715494)\n",
      "\n",
      "Value 12: (gtsam::Pose2)\n",
      "(0.407991647, -8.18623381, -0.124139994)\n",
      "\n",
      "Value 13: (gtsam::Pose2)\n",
      "(0.922180859, -8.23847459, -0.0733692627)\n",
      "\n",
      "Value 14: (gtsam::Pose2)\n",
      "(1.34127284, -8.22007355, -0.11419823)\n",
      "\n",
      "Value 15: (gtsam::Pose2)\n",
      "(1.7707866, -8.25295737, -0.112758043)\n",
      "\n",
      "Value 16: (gtsam::Pose2)\n",
      "(2.14390395, -8.32858807, -0.0876229525)\n",
      "\n",
      "Value 17: (gtsam::Pose2)\n",
      "(2.65697514, -8.34610936, -0.0959090531)\n",
      "\n",
      "Value 18: (gtsam::Pose2)\n",
      "(3.12540662, -8.42564683, -0.0806114596)\n",
      "\n",
      "Value 19: (gtsam::Pose2)\n",
      "(3.53363016, -8.45890751, -0.0845325988)\n",
      "\n",
      "Value 20: (gtsam::Pose2)\n",
      "(3.55683944, -8.46999563, 0.0342009698)\n",
      "\n",
      "Value 21: (gtsam::Pose2)\n",
      "(3.96273767, -8.43099755, 0.140472529)\n",
      "\n",
      "Value 22: (gtsam::Pose2)\n",
      "(4.31055705, -8.34624099, 0.306897353)\n",
      "\n",
      "Value 23: (gtsam::Pose2)\n",
      "(4.66861969, -8.19135328, 0.428578304)\n",
      "\n",
      "Value 24: (gtsam::Pose2)\n",
      "(5.03754132, -7.96849122, 0.602938653)\n",
      "\n",
      "Value 25: (gtsam::Pose2)\n",
      "(5.41233924, -7.65622889, 0.770309764)\n",
      "\n",
      "Value 26: (gtsam::Pose2)\n",
      "(5.66194372, -7.35411594, 0.906803442)\n",
      "\n",
      "Value 27: (gtsam::Pose2)\n",
      "(5.9274912, -6.99096758, 1.07690451)\n",
      "\n",
      "Value 28: (gtsam::Pose2)\n",
      "(6.09008743, -6.54336776, 1.2495313)\n",
      "\n",
      "Value 29: (gtsam::Pose2)\n",
      "(6.20229411, -6.10340493, 1.4387353)\n",
      "\n",
      "Value 30: (gtsam::Pose2)\n",
      "(6.24967981, -5.69402071, 1.61699042)\n",
      "\n",
      "Value 31: (gtsam::Pose2)\n",
      "(6.20569514, -5.25605864, 1.74942374)\n",
      "\n",
      "Value 32: (gtsam::Pose2)\n",
      "(6.17210118, -4.86008648, 1.89654021)\n",
      "\n",
      "Value 33: (gtsam::Pose2)\n",
      "(5.96095785, -4.53112852, 2.04494094)\n",
      "\n",
      "Value 34: (gtsam::Pose2)\n",
      "(5.74063783, -4.23371333, 2.2050316)\n",
      "\n",
      "Value 35: (gtsam::Pose2)\n",
      "(5.46391814, -3.92463408, 2.37665799)\n",
      "\n",
      "Value 36: (gtsam::Pose2)\n",
      "(5.13171927, -3.69272355, 2.5577024)\n",
      "\n",
      "Value 37: (gtsam::Pose2)\n",
      "(4.78485535, -3.49823781, 2.69471833)\n",
      "\n",
      "Value 38: (gtsam::Pose2)\n",
      "(4.38187154, -3.36749422, 2.8795879)\n",
      "\n",
      "Value 39: (gtsam::Pose2)\n",
      "(3.95876702, -3.28540084, 3.00962762)\n",
      "\n",
      "Value 40: (gtsam::Pose2)\n",
      "(3.96480974, -3.28711286, 3.08221572)\n",
      "\n",
      "Value 41: (gtsam::Pose2)\n",
      "(3.46368547, -3.25009415, -3.14000421)\n",
      "\n",
      "Value 42: (gtsam::Pose2)\n",
      "(2.95083051, -3.20041978, 3.13016451)\n",
      "\n",
      "Value 43: (gtsam::Pose2)\n",
      "(2.49304555, -3.22158662, -3.111783)\n",
      "\n",
      "Value 44: (gtsam::Pose2)\n",
      "(2.00734156, -3.25448373, -3.1180877)\n",
      "\n",
      "Value 45: (gtsam::Pose2)\n",
      "(1.4964628, -3.31848044, -3.12857114)\n",
      "\n",
      "Value 46: (gtsam::Pose2)\n",
      "(1.03377674, -3.34605571, -3.11307435)\n",
      "\n",
      "Value 47: (gtsam::Pose2)\n",
      "(0.514652286, -3.39167045, -3.1335088)\n",
      "\n",
      "Value 48: (gtsam::Pose2)\n",
      "(0.0738995705, -3.42646172, 3.13741699)\n",
      "\n",
      "Value 49: (gtsam::Pose2)\n",
      "(-0.389728021, -3.38947674, -3.11191711)\n",
      "\n",
      "Value 50: (gtsam::Pose2)\n",
      "(-0.894660865, -3.41355636, -3.08640551)\n",
      "\n",
      "Value 51: (gtsam::Pose2)\n",
      "(-1.37548535, -3.40631376, -3.11199404)\n",
      "\n",
      "Value 52: (gtsam::Pose2)\n",
      "(-1.84240207, -3.41140118, -3.13872332)\n",
      "\n",
      "Value 53: (gtsam::Pose2)\n",
      "(-2.34360045, -3.39841376, -3.1411227)\n",
      "\n",
      "Value 54: (gtsam::Pose2)\n",
      "(-2.86307435, -3.4406962, -3.09444456)\n",
      "\n",
      "Value 55: (gtsam::Pose2)\n",
      "(-3.36566228, -3.47078803, -3.10969597)\n",
      "\n",
      "Value 56: (gtsam::Pose2)\n",
      "(-3.89410653, -3.53362648, -3.12750403)\n",
      "\n",
      "Value 57: (gtsam::Pose2)\n",
      "(-4.4171171, -3.53110749, -3.13263747)\n",
      "\n",
      "Value 58: (gtsam::Pose2)\n",
      "(-4.92273315, -3.52359971, -3.10578434)\n",
      "\n",
      "Value 59: (gtsam::Pose2)\n",
      "(-5.37831269, -3.51904493, -3.10936669)\n",
      "\n",
      "Value 60: (gtsam::Pose2)\n",
      "(-5.34663565, -3.52884851, -3.07214033)\n",
      "\n",
      "Value 61: (gtsam::Pose2)\n",
      "(-5.77966282, -3.51565934, -2.99382424)\n",
      "\n",
      "Value 62: (gtsam::Pose2)\n",
      "(-6.20306722, -3.68137114, -2.79614822)\n",
      "\n",
      "Value 63: (gtsam::Pose2)\n",
      "(-6.63868537, -3.87799314, -2.56732917)\n",
      "\n",
      "Value 64: (gtsam::Pose2)\n",
      "(-6.976756, -4.12994896, -2.44044467)\n",
      "\n",
      "Value 65: (gtsam::Pose2)\n",
      "(-7.26557943, -4.46156335, -2.29411343)\n",
      "\n",
      "Value 66: (gtsam::Pose2)\n",
      "(-7.52530994, -4.78828973, -2.13871003)\n",
      "\n",
      "Value 67: (gtsam::Pose2)\n",
      "(-7.75021388, -5.1314164, -1.931934)\n",
      "\n",
      "Value 68: (gtsam::Pose2)\n",
      "(-7.87336805, -5.47912328, -1.75627478)\n",
      "\n",
      "Value 69: (gtsam::Pose2)\n",
      "(-7.92918324, -5.93494094, -1.59942518)\n",
      "\n",
      "Value 70: (gtsam::Pose2)\n",
      "(-7.91386572, -6.36498585, -1.40777593)\n",
      "\n",
      "Value 71: (gtsam::Pose2)\n",
      "(-7.84094167, -6.77200635, -1.25660883)\n",
      "\n",
      "Value 72: (gtsam::Pose2)\n",
      "(-7.63899912, -7.15939474, -1.04326552)\n",
      "\n",
      "Value 73: (gtsam::Pose2)\n",
      "(-7.43691664, -7.52307037, -0.91943453)\n",
      "\n",
      "Value 74: (gtsam::Pose2)\n",
      "(-7.12022152, -7.89993237, -0.740350362)\n",
      "\n",
      "Value 75: (gtsam::Pose2)\n",
      "(-6.75167924, -8.21628513, -0.5808406)\n",
      "\n",
      "Value 76: (gtsam::Pose2)\n",
      "(-6.40705964, -8.43939342, -0.391500952)\n",
      "\n",
      "Value 77: (gtsam::Pose2)\n",
      "(-5.98985953, -8.63406821, -0.19670265)\n",
      "\n",
      "Value 78: (gtsam::Pose2)\n",
      "(-5.56910155, -8.67614167, -0.0254195984)\n",
      "\n",
      "Value 79: (gtsam::Pose2)\n",
      "(-5.17981311, -8.65989642, 0.0275264958)\n",
      "\n",
      "Value 80: (gtsam::Pose2)\n",
      "(-5.12418216, -8.59276442, 0.112720988)\n",
      "\n",
      "Value 81: (gtsam::Pose2)\n",
      "(-4.61671792, -8.50041585, 0.107020933)\n",
      "\n",
      "Value 82: (gtsam::Pose2)\n",
      "(-4.02410964, -8.37734968, 0.157208126)\n",
      "\n",
      "Value 83: (gtsam::Pose2)\n",
      "(-3.48791587, -8.3226704, 0.137316626)\n",
      "\n",
      "Value 84: (gtsam::Pose2)\n",
      "(-2.94216256, -8.27523236, 0.173117681)\n",
      "\n",
      "Value 85: (gtsam::Pose2)\n",
      "(-2.38077905, -8.17809872, 0.161105892)\n",
      "\n",
      "Value 86: (gtsam::Pose2)\n",
      "(-1.82530746, -8.0459315, 0.103212031)\n",
      "\n",
      "Value 87: (gtsam::Pose2)\n",
      "(-1.27032679, -7.99750874, 0.0808851405)\n",
      "\n",
      "Value 88: (gtsam::Pose2)\n",
      "(-0.732655239, -7.94987526, 0.136972106)\n",
      "\n",
      "Value 89: (gtsam::Pose2)\n",
      "(-0.184350403, -7.88731446, 0.100156402)\n",
      "\n",
      "Value 90: (gtsam::Pose2)\n",
      "(0.38825227, -7.85244183, 0.0967440492)\n",
      "\n",
      "Value 91: (gtsam::Pose2)\n",
      "(0.956821251, -7.79568542, 0.08515719)\n",
      "\n",
      "Value 92: (gtsam::Pose2)\n",
      "(1.49737315, -7.72249345, 0.0845681033)\n",
      "\n",
      "Value 93: (gtsam::Pose2)\n",
      "(2.05159026, -7.66278136, 0.0745319227)\n",
      "\n",
      "Value 94: (gtsam::Pose2)\n",
      "(2.61049256, -7.62937539, 0.108547222)\n",
      "\n",
      "Value 95: (gtsam::Pose2)\n",
      "(3.1778449, -7.61976531, 0.102326604)\n",
      "\n",
      "Value 96: (gtsam::Pose2)\n",
      "(3.75872146, -7.56653741, 0.0896510816)\n",
      "\n",
      "Value 97: (gtsam::Pose2)\n",
      "(4.33911133, -7.54697473, 0.0916021284)\n",
      "\n",
      "Value 98: (gtsam::Pose2)\n",
      "(4.90209822, -7.46959377, 0.0606016233)\n",
      "\n",
      "Value 99: (gtsam::Pose2)\n",
      "(5.41105533, -7.39379316, 0.0372489687)\n",
      "\n",
      "Value 100: (gtsam::Pose2)\n",
      "(5.40564448, -7.32100745, 0.105964998)\n",
      "\n",
      "Value 101: (gtsam::Pose2)\n",
      "(5.77453778, -7.19769501, 0.219015869)\n",
      "\n",
      "Value 102: (gtsam::Pose2)\n",
      "(6.19273132, -6.9815322, 0.382104878)\n",
      "\n",
      "Value 103: (gtsam::Pose2)\n",
      "(6.63146637, -6.76513467, 0.539182521)\n",
      "\n",
      "Value 104: (gtsam::Pose2)\n",
      "(6.99657753, -6.4728512, 0.743000524)\n",
      "\n",
      "Value 105: (gtsam::Pose2)\n",
      "(7.33545685, -6.13186719, 0.905925317)\n",
      "\n",
      "Value 106: (gtsam::Pose2)\n",
      "(7.60214837, -5.70238088, 1.07419847)\n",
      "\n",
      "Value 107: (gtsam::Pose2)\n",
      "(7.76383115, -5.23855851, 1.27824481)\n",
      "\n",
      "Value 108: (gtsam::Pose2)\n",
      "(7.86306375, -4.82143383, 1.46615582)\n",
      "\n",
      "Value 109: (gtsam::Pose2)\n",
      "(7.9122507, -4.34378238, 1.6178521)\n",
      "\n",
      "Value 110: (gtsam::Pose2)\n",
      "(7.82470302, -3.90990551, 1.750709)\n",
      "\n",
      "Value 111: (gtsam::Pose2)\n",
      "(7.69177028, -3.39990696, 1.93465848)\n",
      "\n",
      "Value 112: (gtsam::Pose2)\n",
      "(7.45775225, -2.9419036, 2.12265971)\n",
      "\n",
      "Value 113: (gtsam::Pose2)\n",
      "(7.1839656, -2.59464424, 2.25652209)\n",
      "\n",
      "Value 114: (gtsam::Pose2)\n",
      "(6.87434868, -2.27646387, 2.39237994)\n",
      "\n",
      "Value 115: (gtsam::Pose2)\n",
      "(6.49220951, -1.99014663, 2.50213888)\n",
      "\n",
      "Value 116: (gtsam::Pose2)\n",
      "(6.11087724, -1.71813193, 2.68518853)\n",
      "\n",
      "Value 117: (gtsam::Pose2)\n",
      "(5.72172372, -1.55639306, 2.86335969)\n",
      "\n",
      "Value 118: (gtsam::Pose2)\n",
      "(5.2404821, -1.41035692, 3.01859381)\n",
      "\n",
      "Value 119: (gtsam::Pose2)\n",
      "(4.80074641, -1.42642598, -0.0473971003)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "initial.print(\"Initial Estimate:\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result:\n",
      "\n",
      "Values with 120 values:\n",
      "Value 0: (gtsam::Pose2)\n",
      "(-5, -8, 6.30433605e-36)\n",
      "\n",
      "Value 1: (gtsam::Pose2)\n",
      "(-4.54868009, -7.99833721, 0.000910991209)\n",
      "\n",
      "Value 2: (gtsam::Pose2)\n",
      "(-4.09736161, -7.99626349, 0.00107372774)\n",
      "\n",
      "Value 3: (gtsam::Pose2)\n",
      "(-3.64600033, -7.99761368, 0.00366244327)\n",
      "\n",
      "Value 4: (gtsam::Pose2)\n",
      "(-3.19464332, -7.99780007, 0.00707681159)\n",
      "\n",
      "Value 5: (gtsam::Pose2)\n",
      "(-2.73948036, -7.97983811, -0.0211718037)\n",
      "\n",
      "Value 6: (gtsam::Pose2)\n",
      "(-2.26528003, -8.00258301, -0.0670592183)\n",
      "\n",
      "Value 7: (gtsam::Pose2)\n",
      "(-1.82970101, -8.01584261, -0.0552010389)\n",
      "\n",
      "Value 8: (gtsam::Pose2)\n",
      "(-1.4089618, -8.02256837, -0.0550078948)\n",
      "\n",
      "Value 9: (gtsam::Pose2)\n",
      "(-0.943197679, -8.03905694, -0.0904537955)\n",
      "\n",
      "Value 10: (gtsam::Pose2)\n",
      "(-0.483031496, -8.09978878, -0.0445171911)\n",
      "\n",
      "Value 11: (gtsam::Pose2)\n",
      "(-0.00872590098, -8.12003399, -0.0725295265)\n",
      "\n",
      "Value 12: (gtsam::Pose2)\n",
      "(0.445078241, -8.14861451, -0.107120007)\n",
      "\n",
      "Value 13: (gtsam::Pose2)\n",
      "(0.963671684, -8.19508483, -0.056710861)\n",
      "\n",
      "Value 14: (gtsam::Pose2)\n",
      "(1.38598459, -8.17269825, -0.096558589)\n",
      "\n",
      "Value 15: (gtsam::Pose2)\n",
      "(1.82394563, -8.19890158, -0.0960568972)\n",
      "\n",
      "Value 16: (gtsam::Pose2)\n",
      "(2.20620825, -8.26918956, -0.0720707566)\n",
      "\n",
      "Value 17: (gtsam::Pose2)\n",
      "(2.721566, -8.27522674, -0.0818811825)\n",
      "\n",
      "Value 18: (gtsam::Pose2)\n",
      "(3.19313692, -8.34467885, -0.069902864)\n",
      "\n",
      "Value 19: (gtsam::Pose2)\n",
      "(3.60308679, -8.36502943, -0.0742848844)\n",
      "\n",
      "Value 20: (gtsam::Pose2)\n",
      "(3.62780019, -8.36734206, 0.0437736037)\n",
      "\n",
      "Value 21: (gtsam::Pose2)\n",
      "(4.03670095, -8.31661393, 0.154016099)\n",
      "\n",
      "Value 22: (gtsam::Pose2)\n",
      "(4.38671596, -8.21929997, 0.321988097)\n",
      "\n",
      "Value 23: (gtsam::Pose2)\n",
      "(4.7489331, -8.05467972, 0.449142824)\n",
      "\n",
      "Value 24: (gtsam::Pose2)\n",
      "(5.11972613, -7.819931, 0.628879792)\n",
      "\n",
      "Value 25: (gtsam::Pose2)\n",
      "(5.48977176, -7.49535236, 0.801468959)\n",
      "\n",
      "Value 26: (gtsam::Pose2)\n",
      "(5.73331748, -7.18291152, 0.94359957)\n",
      "\n",
      "Value 27: (gtsam::Pose2)\n",
      "(5.98173161, -6.81015139, 1.11521152)\n",
      "\n",
      "Value 28: (gtsam::Pose2)\n",
      "(6.12347224, -6.35657011, 1.28770708)\n",
      "\n",
      "Value 29: (gtsam::Pose2)\n",
      "(6.21111376, -5.90853813, 1.47452488)\n",
      "\n",
      "Value 30: (gtsam::Pose2)\n",
      "(6.23611553, -5.4936387, 1.64710352)\n",
      "\n",
      "Value 31: (gtsam::Pose2)\n",
      "(6.16865056, -5.04227012, 1.78681967)\n",
      "\n",
      "Value 32: (gtsam::Pose2)\n",
      "(6.10991138, -4.63293647, 1.93782528)\n",
      "\n",
      "Value 33: (gtsam::Pose2)\n",
      "(5.87867657, -4.29775177, 2.08631622)\n",
      "\n",
      "Value 34: (gtsam::Pose2)\n",
      "(5.63957329, -3.99447192, 2.24810587)\n",
      "\n",
      "Value 35: (gtsam::Pose2)\n",
      "(5.344929, -3.6853299, 2.41686433)\n",
      "\n",
      "Value 36: (gtsam::Pose2)\n",
      "(4.99884327, -3.45467922, 2.59817485)\n",
      "\n",
      "Value 37: (gtsam::Pose2)\n",
      "(4.64123496, -3.26599461, 2.73447191)\n",
      "\n",
      "Value 38: (gtsam::Pose2)\n",
      "(4.23023981, -3.14296779, 2.92168499)\n",
      "\n",
      "Value 39: (gtsam::Pose2)\n",
      "(3.80310605, -3.07863666, 3.04514491)\n",
      "\n",
      "Value 40: (gtsam::Pose2)\n",
      "(3.80825652, -3.08001633, 3.11115389)\n",
      "\n",
      "Value 41: (gtsam::Pose2)\n",
      "(3.30532174, -3.057396, -3.11760478)\n",
      "\n",
      "Value 42: (gtsam::Pose2)\n",
      "(2.79053363, -3.01910404, -3.13713316)\n",
      "\n",
      "Value 43: (gtsam::Pose2)\n",
      "(2.33219352, -3.04742419, -3.10232394)\n",
      "\n",
      "Value 44: (gtsam::Pose2)\n",
      "(1.84587323, -3.08479715, -3.11496198)\n",
      "\n",
      "Value 45: (gtsam::Pose2)\n",
      "(1.33424782, -3.15027337, -3.13165377)\n",
      "\n",
      "Value 46: (gtsam::Pose2)\n",
      "(0.870529778, -3.17630511, -3.12228364)\n",
      "\n",
      "Value 47: (gtsam::Pose2)\n",
      "(0.350058105, -3.21702, 3.13444315)\n",
      "\n",
      "Value 48: (gtsam::Pose2)\n",
      "(-0.0921225901, -3.24497606, 3.11624044)\n",
      "\n",
      "Value 49: (gtsam::Pose2)\n",
      "(-0.555812224, -3.19806477, -3.13902435)\n",
      "\n",
      "Value 50: (gtsam::Pose2)\n",
      "(-1.06216136, -3.20833252, -3.11937144)\n",
      "\n",
      "Value 51: (gtsam::Pose2)\n",
      "(-1.54343502, -3.1851285, 3.13240378)\n",
      "\n",
      "Value 52: (gtsam::Pose2)\n",
      "(-2.01114696, -3.17198866, 3.099898)\n",
      "\n",
      "Value 53: (gtsam::Pose2)\n",
      "(-2.5122183, -3.13656865, 3.0917501)\n",
      "\n",
      "Value 54: (gtsam::Pose2)\n",
      "(-3.03411041, -3.15255503, 3.13275905)\n",
      "\n",
      "Value 55: (gtsam::Pose2)\n",
      "(-3.53854382, -3.15436108, 3.11190217)\n",
      "\n",
      "Value 56: (gtsam::Pose2)\n",
      "(-4.0708029, -3.18443789, 3.08858259)\n",
      "\n",
      "Value 57: (gtsam::Pose2)\n",
      "(-4.59341676, -3.14673985, 3.07796612)\n",
      "\n",
      "Value 58: (gtsam::Pose2)\n",
      "(-5.09810622, -3.10246782, 3.09935627)\n",
      "\n",
      "Value 59: (gtsam::Pose2)\n",
      "(-5.55289298, -3.06228966, 3.09032875)\n",
      "\n",
      "Value 60: (gtsam::Pose2)\n",
      "(-5.52309292, -3.07458297, 3.12211793)\n",
      "\n",
      "Value 61: (gtsam::Pose2)\n",
      "(-5.95418679, -3.02287105, -3.08818453)\n",
      "\n",
      "Value 62: (gtsam::Pose2)\n",
      "(-6.39227011, -3.14783442, -2.89576938)\n",
      "\n",
      "Value 63: (gtsam::Pose2)\n",
      "(-6.84623281, -3.30003839, -2.67201103)\n",
      "\n",
      "Value 64: (gtsam::Pose2)\n",
      "(-7.20972886, -3.51517135, -2.54993851)\n",
      "\n",
      "Value 65: (gtsam::Pose2)\n",
      "(-7.5340089, -3.81311998, -2.40809681)\n",
      "\n",
      "Value 66: (gtsam::Pose2)\n",
      "(-7.83016372, -4.10806635, -2.2568668)\n",
      "\n",
      "Value 67: (gtsam::Pose2)\n",
      "(-8.09489691, -4.4221692, -2.05393371)\n",
      "\n",
      "Value 68: (gtsam::Pose2)\n",
      "(-8.26039957, -4.75218453, -1.88178409)\n",
      "\n",
      "Value 69: (gtsam::Pose2)\n",
      "(-8.3737838, -5.19730979, -1.72800798)\n",
      "\n",
      "Value 70: (gtsam::Pose2)\n",
      "(-8.41468563, -5.62564827, -1.53902093)\n",
      "\n",
      "Value 71: (gtsam::Pose2)\n",
      "(-8.39660363, -6.03859098, -1.39012661)\n",
      "\n",
      "Value 72: (gtsam::Pose2)\n",
      "(-8.24897679, -6.44929328, -1.1786847)\n",
      "\n",
      "Value 73: (gtsam::Pose2)\n",
      "(-8.09879131, -6.83680022, -1.05640604)\n",
      "\n",
      "Value 74: (gtsam::Pose2)\n",
      "(-7.83746902, -7.25325345, -0.878511349)\n",
      "\n",
      "Value 75: (gtsam::Pose2)\n",
      "(-7.51695584, -7.61722591, -0.719885256)\n",
      "\n",
      "Value 76: (gtsam::Pose2)\n",
      "(-7.20753289, -7.88582192, -0.531212565)\n",
      "\n",
      "Value 77: (gtsam::Pose2)\n",
      "(-6.82245633, -8.13657563, -0.336890732)\n",
      "\n",
      "Value 78: (gtsam::Pose2)\n",
      "(-6.41265348, -8.23690606, -0.166039367)\n",
      "\n",
      "Value 79: (gtsam::Pose2)\n",
      "(-6.02587919, -8.27526008, -0.113536126)\n",
      "\n",
      "Value 80: (gtsam::Pose2)\n",
      "(-5.96231087, -8.21649389, -0.0288480378)\n",
      "\n",
      "Value 81: (gtsam::Pose2)\n",
      "(-5.44915593, -8.19821793, -0.0372807619)\n",
      "\n",
      "Value 82: (gtsam::Pose2)\n",
      "(-4.84727372, -8.16319408, 0.0110219071)\n",
      "\n",
      "Value 83: (gtsam::Pose2)\n",
      "(-4.31115252, -8.18525481, -0.0107238248)\n",
      "\n",
      "Value 84: (gtsam::Pose2)\n",
      "(-3.7666918, -8.21688826, 0.022233021)\n",
      "\n",
      "Value 85: (gtsam::Pose2)\n",
      "(-3.20179892, -8.20490028, 0.00868713744)\n",
      "\n",
      "Value 86: (gtsam::Pose2)\n",
      "(-2.63741324, -8.15826309, -0.0511571504)\n",
      "\n",
      "Value 87: (gtsam::Pose2)\n",
      "(-2.08731771, -8.19331808, -0.0749347732)\n",
      "\n",
      "Value 88: (gtsam::Pose2)\n",
      "(-1.55450173, -8.22727925, -0.0214032543)\n",
      "\n",
      "Value 89: (gtsam::Pose2)\n",
      "(-1.00730343, -8.25085103, -0.0578263565)\n",
      "\n",
      "Value 90: (gtsam::Pose2)\n",
      "(-0.440456776, -8.30537411, -0.061259167)\n",
      "\n",
      "Value 91: (gtsam::Pose2)\n",
      "(0.125998232, -8.33804063, -0.0748914481)\n",
      "\n",
      "Value 92: (gtsam::Pose2)\n",
      "(0.6673437, -8.35118477, -0.077882377)\n",
      "\n",
      "Value 93: (gtsam::Pose2)\n",
      "(1.21938157, -8.37878826, -0.0888974945)\n",
      "\n",
      "Value 94: (gtsam::Pose2)\n",
      "(1.77172723, -8.43366116, -0.0573312904)\n",
      "\n",
      "Value 95: (gtsam::Pose2)\n",
      "(2.32399889, -8.51681684, -0.0617856078)\n",
      "\n",
      "Value 96: (gtsam::Pose2)\n",
      "(2.8968868, -8.55816098, -0.072918293)\n",
      "\n",
      "Value 97: (gtsam::Pose2)\n",
      "(3.46974824, -8.63616063, -0.0673410381)\n",
      "\n",
      "Value 98: (gtsam::Pose2)\n",
      "(4.03483751, -8.65222155, -0.0927584539)\n",
      "\n",
      "Value 99: (gtsam::Pose2)\n",
      "(4.54699101, -8.6634539, -0.10855035)\n",
      "\n",
      "Value 100: (gtsam::Pose2)\n",
      "(4.54980376, -8.59904987, -0.0324009899)\n",
      "\n",
      "Value 101: (gtsam::Pose2)\n",
      "(4.9278504, -8.5355228, 0.0828768894)\n",
      "\n",
      "Value 102: (gtsam::Pose2)\n",
      "(5.36716178, -8.38583213, 0.250946396)\n",
      "\n",
      "Value 103: (gtsam::Pose2)\n",
      "(5.82297909, -8.23294344, 0.406478203)\n",
      "\n",
      "Value 104: (gtsam::Pose2)\n",
      "(6.21610349, -7.99581149, 0.608656643)\n",
      "\n",
      "Value 105: (gtsam::Pose2)\n",
      "(6.59320358, -7.70590213, 0.767556601)\n",
      "\n",
      "Value 106: (gtsam::Pose2)\n",
      "(6.91218926, -7.31992132, 0.930931388)\n",
      "\n",
      "Value 107: (gtsam::Pose2)\n",
      "(7.14108457, -6.88388251, 1.12991728)\n",
      "\n",
      "Value 108: (gtsam::Pose2)\n",
      "(7.30351768, -6.48595273, 1.31381426)\n",
      "\n",
      "Value 109: (gtsam::Pose2)\n",
      "(7.4313441, -6.02531549, 1.46780267)\n",
      "\n",
      "Value 110: (gtsam::Pose2)\n",
      "(7.41635155, -5.58726398, 1.60584124)\n",
      "\n",
      "Value 111: (gtsam::Pose2)\n",
      "(7.3678351, -5.07819572, 1.7948244)\n",
      "\n",
      "Value 112: (gtsam::Pose2)\n",
      "(7.20930535, -4.60684416, 1.98998957)\n",
      "\n",
      "Value 113: (gtsam::Pose2)\n",
      "(6.98943644, -4.24158177, 2.1351762)\n",
      "\n",
      "Value 114: (gtsam::Pose2)\n",
      "(6.72621806, -3.90341335, 2.28032773)\n",
      "\n",
      "Value 115: (gtsam::Pose2)\n",
      "(6.3822616, -3.58835495, 2.39974786)\n",
      "\n",
      "Value 116: (gtsam::Pose2)\n",
      "(6.03453796, -3.2909697, 2.58938803)\n",
      "\n",
      "Value 117: (gtsam::Pose2)\n",
      "(5.66481945, -3.10102709, 2.77060382)\n",
      "\n",
      "Value 118: (gtsam::Pose2)\n",
      "(5.20138091, -2.91931592, 2.92544963)\n",
      "\n",
      "Value 119: (gtsam::Pose2)\n",
      "(4.7620568, -2.89441571, -0.140541286)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result.print(\"Result:\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120\n"
     ]
    }
   ],
   "source": [
    "X_gtsam_opt = []\n",
    "Y_gtsam_opt = []\n",
    "THETA_gtsam_opt = []\n",
    "\n",
    "## Alternate Way\n",
    "# for key in result.keys():\n",
    "#     # print(key, result.atPose2(key).x(), result.atPose2(key).y(), result.atPose2(key).theta())\n",
    "#     X_gtsam_opt.append(result.atPose2(key).x())\n",
    "#     Y_gtsam_opt.append(result.atPose2(key).y())\n",
    "#     THETA_gtsam_opt.append(result.atPose2(key).theta())\n",
    "\n",
    "arr_opt = gtsam.utilities.extractPose2(result)\n",
    "for opt_mes in arr_opt:\n",
    "    X_gtsam_opt.append(opt_mes[0])\n",
    "    Y_gtsam_opt.append(opt_mes[1])\n",
    "    THETA_gtsam_opt.append(opt_mes[2])\n",
    "    \n",
    "print(len(X_gtsam_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimize the pose graph using g2o binary and loads the optimized graph.  \n",
    "G2O provides options to change the number of iteration steps, change the optimization solver and incorporate robust cost functions to minimize the impact of false loop closure constraints due to error in frontend.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing Ground Truth and optimized trajectory.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimize()\n",
    "# (xOpt, yOpt, tOpt) = readG2o(\"opt.g2o\")\n",
    "drawTwo(X, Y, THETA, X_gtsam_opt, Y_gtsam_opt, THETA_gtsam_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing Ground Truth, optimized and noisy trajectory.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawThree(X, Y, THETA, X_gtsam_opt, Y_gtsam_opt, THETA_gtsam_opt, xN, yN, tN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeG2o(graph, initial, \"graph.g2o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(NonlinearFactorGraph: size: 139\n",
      "\n",
      "Factor 0: BetweenFactor(0,1)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 1: BetweenFactor(1,2)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 2: BetweenFactor(2,3)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 3: BetweenFactor(3,4)\n",
      "  measured:  (0.45, 0, 0)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 4: BetweenFactor(4,5)\n",
      "  measured:  (0.451517, 0.0149985, -0.0298773)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 5: BetweenFactor(5,6)\n",
      "  measured:  (0.470808, -0.012549, -0.0475373)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 6: BetweenFactor(6,7)\n",
      "  measured:  (0.430569, 0.0179573, 0.0099675)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 7: BetweenFactor(7,8)\n",
      "  measured:  (0.415576, 0.0185601, -0.00263961)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 8: BetweenFactor(8,9)\n",
      "  measured:  (0.462752, 0.00996759, -0.0347045)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 9: BetweenFactor(9,10)\n",
      "  measured:  (0.46053, -0.0182066, 0.0464094)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 10: BetweenFactor(10,11)\n",
      "  measured:  (0.4717, 0.00138407, -0.0294897)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 11: BetweenFactor(11,12)\n",
      "  measured:  (0.451633, 0.00479679, -0.0362684)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 12: BetweenFactor(12,13)\n",
      "  measured:  (0.516701, 0.0118289, 0.0507707)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 13: BetweenFactor(13,14)\n",
      "  measured:  (0.416616, 0.0490724, -0.040829)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 14: BetweenFactor(14,15)\n",
      "  measured:  (0.430463, 0.0162735, 0.00144019)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 15: BetweenFactor(15,16)\n",
      "  measured:  (0.379258, -0.0331675, 0.0251351)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 16: BetweenFactor(16,17)\n",
      "  measured:  (0.512636, 0.0274452, -0.0082861)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 17: BetweenFactor(17,18)\n",
      "  measured:  (0.473895, -0.034314, 0.0152976)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 18: BetweenFactor(18,19)\n",
      "  measured:  (0.409576, -0.000280803, -0.00392114)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 19: BetweenFactor(19,20)\n",
      "  measured:  (0.0240626, -0.00908892, 0.118734)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 20: BetweenFactor(20,21)\n",
      "  measured:  (0.406994, 0.0250959, 0.106272)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 21: BetweenFactor(21,22)\n",
      "  measured:  (0.35626, 0.0352232, 0.166425)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 22: BetweenFactor(22,23)\n",
      "  measured:  (0.388124, 0.0394791, 0.121681)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 23: BetweenFactor(23,24)\n",
      "  measured:  (0.428172, 0.0493901, 0.17436)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 24: BetweenFactor(24,25)\n",
      "  measured:  (0.485784, 0.044667, 0.167371)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 25: BetweenFactor(25,26)\n",
      "  measured:  (0.389519, 0.043011, 0.136494)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 26: BetweenFactor(26,27)\n",
      "  measured:  (0.449641, 0.0146674, 0.170101)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 27: BetweenFactor(27,28)\n",
      "  measured:  (0.471189, 0.0690224, 0.172627)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 28: BetweenFactor(28,29)\n",
      "  measured:  (0.452884, 0.03246, 0.189204)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 29: BetweenFactor(29,30)\n",
      "  measured:  (0.412059, 0.0069336, 0.178255)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 30: BetweenFactor(30,31)\n",
      "  measured:  (0.439526, 0.0237137, 0.132433)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 31: BetweenFactor(31,32)\n",
      "  measured:  (0.395641, -0.0372965, 0.147116)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 32: BetweenFactor(32,33)\n",
      "  measured:  (0.379228, 0.0947689, 0.148401)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 33: BetweenFactor(33,34)\n",
      "  measured:  (0.365199, 0.060222, 0.160091)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 34: BetweenFactor(34,35)\n",
      "  measured:  (0.412945, 0.0397561, 0.171626)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 35: BetweenFactor(35,36)\n",
      "  measured:  (0.400253, 0.0627377, 0.181044)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 36: BetweenFactor(36,37)\n",
      "  measured:  (0.396612, 0.0289528, 0.137016)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 37: BetweenFactor(37,38)\n",
      "  measured:  (0.419912, 0.0562442, 0.18487)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 38: BetweenFactor(38,39)\n",
      "  measured:  (0.429929, 0.0302997, 0.13004)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 39: BetweenFactor(39,40)\n",
      "  measured:  (-0.00621545, 0.000902012, 0.0725881)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 40: BetweenFactor(40,41)\n",
      "  measured:  (0.502438, -0.00721573, 0.0609654)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 41: BetweenFactor(41,42)\n",
      "  measured:  (0.512775, -0.0504889, -0.0130166)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 42: BetweenFactor(42,43)\n",
      "  measured:  (0.457513, 0.026397, 0.0412378)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 43: BetweenFactor(43,44)\n",
      "  measured:  (0.486469, 0.018406, -0.0063047)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 44: BetweenFactor(44,45)\n",
      "  measured:  (0.512242, 0.051972, -0.0104834)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 45: BetweenFactor(45,46)\n",
      "  measured:  (0.463006, 0.0215482, 0.0154968)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 46: BetweenFactor(46,47)\n",
      "  measured:  (0.520214, 0.0307936, -0.0204345)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 47: BetweenFactor(47,48)\n",
      "  measured:  (0.44102, 0.0312272, -0.0122595)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 48: BetweenFactor(48,49)\n",
      "  measured:  (0.463778, -0.0350487, 0.0338512)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 49: BetweenFactor(49,50)\n",
      "  measured:  (0.505425, 0.00908706, 0.0255116)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 50: BetweenFactor(50,51)\n",
      "  measured:  (0.479693, -0.0337534, -0.0255885)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 51: BetweenFactor(51,52)\n",
      "  measured:  (0.466863, -0.00873287, -0.0267293)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 52: BetweenFactor(52,53)\n",
      "  measured:  (0.501159, -0.0144255, -0.00239938)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 53: BetweenFactor(53,54)\n",
      "  measured:  (0.519494, 0.0420383, 0.0466781)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 54: BetweenFactor(54,55)\n",
      "  measured:  (0.503448, 0.00637111, -0.0152514)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 55: BetweenFactor(55,56)\n",
      "  measured:  (0.530179, 0.0459537, -0.0178081)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 56: BetweenFactor(56,57)\n",
      "  measured:  (0.522923, -0.009887, -0.00513345)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 57: BetweenFactor(57,58)\n",
      "  measured:  (0.505529, -0.0120353, 0.0268531)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 58: BetweenFactor(58,59)\n",
      "  measured:  (0.455124, -0.0208619, -0.00358234)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 59: BetweenFactor(59,60)\n",
      "  measured:  (-0.0313447, 0.0108191, 0.0372264)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 60: BetweenFactor(60,61)\n",
      "  measured:  (0.431068, -0.0432079, 0.0783161)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 61: BetweenFactor(61,62)\n",
      "  measured:  (0.443188, 0.101568, 0.197676)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 62: BetweenFactor(62,63)\n",
      "  measured:  (0.476463, 0.0374998, 0.228819)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 63: BetweenFactor(63,64)\n",
      "  measured:  (0.420708, 0.0278947, 0.126884)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 64: BetweenFactor(64,65)\n",
      "  measured:  (0.434613, 0.0670686, 0.146331)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 65: BetweenFactor(65,66)\n",
      "  measured:  (0.416828, 0.0215543, 0.155403)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 66: BetweenFactor(66,67)\n",
      "  measured:  (0.410234, -0.00504058, 0.206776)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 67: BetweenFactor(67,68)\n",
      "  measured:  (0.368793, 0.00764815, 0.175659)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 68: BetweenFactor(68,69)\n",
      "  measured:  (0.458293, 0.0292026, 0.15685)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 69: BetweenFactor(69,70)\n",
      "  measured:  (0.42943, 0.0276212, 0.191649)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 70: BetweenFactor(70,71)\n",
      "  measured:  (0.41346, 0.00589806, 0.151167)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 71: BetweenFactor(71,72)\n",
      "  measured:  (0.430834, 0.072337, 0.213343)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 72: BetweenFactor(72,73)\n",
      "  measured:  (0.415964, -0.0084649, 0.123831)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 73: BetweenFactor(73,74)\n",
      "  measured:  (0.491706, 0.0233745, 0.179084)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 74: BetweenFactor(74,75)\n",
      "  measured:  (0.485464, 0.0150571, 0.15951)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 75: BetweenFactor(75,76)\n",
      "  measured:  (0.410528, 0.00258313, 0.18934)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 76: BetweenFactor(76,77)\n",
      "  measured:  (0.459917, -0.0207515, 0.194798)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 77: BetweenFactor(77,78)\n",
      "  measured:  (0.420867, 0.0409694, 0.171283)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 78: BetweenFactor(78,79)\n",
      "  measured:  (0.38875, 0.0261345, 0.0529461)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 79: BetweenFactor(79,80)\n",
      "  measured:  (0.0574575, 0.0655754, 0.0851945)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 80: BetweenFactor(80,81)\n",
      "  measured:  (0.514631, 0.0346817, -0.00570006)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 81: BetweenFactor(81,82)\n",
      "  measured:  (0.602363, 0.0590616, 0.0501872)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 82: BetweenFactor(82,83)\n",
      "  measured:  (0.538142, -0.0299422, -0.0198915)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 83: BetweenFactor(83,84)\n",
      "  measured:  (0.54711, -0.0277142, 0.0358011)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 84: BetweenFactor(84,85)\n",
      "  measured:  (0.569724, -0.00101896, -0.0120118)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 85: BetweenFactor(85,86)\n",
      "  measured:  (0.569479, 0.0413526, -0.0578939)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 86: BetweenFactor(86,87)\n",
      "  measured:  (0.557016, -0.00901396, -0.0223269)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 87: BetweenFactor(87,88)\n",
      "  measured:  (0.539762, 0.00403551, 0.056087)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 88: BetweenFactor(88,89)\n",
      "  measured:  (0.551712, -0.012893, -0.0368157)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 89: BetweenFactor(89,90)\n",
      "  measured:  (0.57322, -0.0225561, -0.00341235)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 90: BetweenFactor(90,91)\n",
      "  measured:  (0.571393, 0.00157112, -0.0115869)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 91: BetweenFactor(91,92)\n",
      "  measured:  (0.544818, 0.0269505, -0.000589087)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 92: BetweenFactor(92,93)\n",
      "  measured:  (0.55728, 0.0126855, -0.0100362)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 93: BetweenFactor(93,94)\n",
      "  measured:  (0.559838, -0.00830427, 0.0340153)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 94: BetweenFactor(94,95)\n",
      "  measured:  (0.565054, -0.0519101, -0.00622062)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 95: BetweenFactor(95,96)\n",
      "  measured:  (0.583275, -0.00638598, -0.0126755)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 96: BetweenFactor(96,97)\n",
      "  measured:  (0.579811, -0.0324788, 0.00195105)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 97: BetweenFactor(97,98)\n",
      "  measured:  (0.567705, 0.0255578, -0.0310005)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 98: BetweenFactor(98,99)\n",
      "  measured:  (0.512614, 0.0448367, -0.0233527)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 99: BetweenFactor(99,100)\n",
      "  measured:  (-0.00269653, 0.0729367, 0.068716)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 100: BetweenFactor(100,101)\n",
      "  measured:  (0.379867, 0.0836041, 0.113051)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 101: BetweenFactor(101,102)\n",
      "  measured:  (0.455169, 0.120139, 0.163089)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 102: BetweenFactor(102,103)\n",
      "  measured:  (0.487783, 0.0371983, 0.157078)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 103: BetweenFactor(103,104)\n",
      "  measured:  (0.463381, 0.0633561, 0.203818)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 104: BetweenFactor(104,105)\n",
      "  measured:  (0.480241, 0.0218629, 0.162925)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 105: BetweenFactor(105,106)\n",
      "  measured:  (0.502542, 0.0550896, 0.168273)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 106: BetweenFactor(106,107)\n",
      "  measured:  (0.484828, 0.0788293, 0.204046)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 107: BetweenFactor(107,108)\n",
      "  measured:  (0.42802, 0.0252809, 0.187911)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 108: BetweenFactor(108,109)\n",
      "  measured:  (0.480176, 0.000972619, 0.151696)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 109: BetweenFactor(109,110)\n",
      "  measured:  (0.437515, 0.0670419, 0.132857)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 110: BetweenFactor(110,111)\n",
      "  measured:  (0.525554, 0.0395261, 0.183949)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 111: BetweenFactor(111,112)\n",
      "  measured:  (0.511301, 0.0556996, 0.188001)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 112: BetweenFactor(112,113)\n",
      "  measured:  (0.439248, 0.0510834, 0.133862)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 113: BetweenFactor(113,114)\n",
      "  measured:  (0.442319, 0.0381481, 0.135858)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 114: BetweenFactor(114,115)\n",
      "  measured:  (0.474812, 0.0506119, 0.109759)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 115: BetweenFactor(115,116)\n",
      "  measured:  (0.468316, 0.00929225, 0.18305)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 116: BetweenFactor(116,117)\n",
      "  measured:  (0.420603, 0.0263252, 0.178171)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 117: BetweenFactor(117,118)\n",
      "  measured:  (0.502844, -0.00824354, 0.155234)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 118: BetweenFactor(118,119)\n",
      "  measured:  (0.434442, 0.0698984, -3.06599)\n",
      "isotropic dim=3 sigma=0.00439999968\n",
      "\n",
      "Factor 119: BetweenFactor(0,80)\n",
      "  measured:  (-0.95, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 120: BetweenFactor(2,82)\n",
      "  measured:  (-0.75, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 121: BetweenFactor(4,84)\n",
      "  measured:  (-0.55, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 122: BetweenFactor(6,86)\n",
      "  measured:  (-0.35, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 123: BetweenFactor(8,88)\n",
      "  measured:  (-0.15, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 124: BetweenFactor(10,90)\n",
      "  measured:  (0.05, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 125: BetweenFactor(12,92)\n",
      "  measured:  (0.25, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 126: BetweenFactor(14,94)\n",
      "  measured:  (0.45, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 127: BetweenFactor(16,96)\n",
      "  measured:  (0.65, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 128: BetweenFactor(18,98)\n",
      "  measured:  (0.85, -0.2, 0)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 129: BetweenFactor(20,100)\n",
      "  measured:  (0.930239, -0.277767, 2.09265e-16)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 130: BetweenFactor(22,102)\n",
      "  measured:  (0.898526, -0.508464, 6.02552e-16)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 131: BetweenFactor(24,104)\n",
      "  measured:  (0.749683, -0.783502, 4.19514e-16)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 132: BetweenFactor(26,106)\n",
      "  measured:  (0.519601, -0.995308, -8.00473e-16)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 133: BetweenFactor(28,108)\n",
      "  measured:  (0.233211, -1.12093, 2.11892e-17)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 134: BetweenFactor(30,110)\n",
      "  measured:  (-0.0784504, -1.14676, -2.20726e-16)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 135: BetweenFactor(32,112)\n",
      "  measured:  (-0.381611, -1.06998, -7.22614e-18)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 136: BetweenFactor(34,114)\n",
      "  measured:  (-0.643417, -0.898938, -2.03587e-18)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 137: BetweenFactor(36,116)\n",
      "  measured:  (-0.8355, -0.65215, 4.17776e-16)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      "Factor 138: BetweenFactor(38,118)\n",
      "  measured:  (-0.937043, -0.356365, -5.09204e-18)\n",
      "isotropic dim=3 sigma=0.0137000056\n",
      "\n",
      ", Values with 120 values:\n",
      "Value 0: (gtsam::Pose2)\n",
      "(-5, -8, 0)\n",
      "\n",
      "Value 1: (gtsam::Pose2)\n",
      "(-4.55, -8, 0)\n",
      "\n",
      "Value 2: (gtsam::Pose2)\n",
      "(-4.1, -8, 0)\n",
      "\n",
      "Value 3: (gtsam::Pose2)\n",
      "(-3.65, -8, 0)\n",
      "\n",
      "Value 4: (gtsam::Pose2)\n",
      "(-3.2, -8, 0)\n",
      "\n",
      "Value 5: (gtsam::Pose2)\n",
      "(-2.74848, -7.985, -0.0298773)\n",
      "\n",
      "Value 6: (gtsam::Pose2)\n",
      "(-2.27826, -8.01161, -0.0774146)\n",
      "\n",
      "Value 7: (gtsam::Pose2)\n",
      "(-1.84759, -8.027, -0.0674471)\n",
      "\n",
      "Value 8: (gtsam::Pose2)\n",
      "(-1.43171, -8.0365, -0.0700867)\n",
      "\n",
      "Value 9: (gtsam::Pose2)\n",
      "(-0.969396, -8.05896, -0.104791)\n",
      "\n",
      "Value 10: (gtsam::Pose2)\n",
      "(-0.513297, -8.12524, -0.0583818)\n",
      "\n",
      "Value 11: (gtsam::Pose2)\n",
      "(-0.0423198, -8.15138, -0.0878715)\n",
      "\n",
      "Value 12: (gtsam::Pose2)\n",
      "(0.407992, -8.18623, -0.12414)\n",
      "\n",
      "Value 13: (gtsam::Pose2)\n",
      "(0.922181, -8.23847, -0.0733693)\n",
      "\n",
      "Value 14: (gtsam::Pose2)\n",
      "(1.34127, -8.22007, -0.114198)\n",
      "\n",
      "Value 15: (gtsam::Pose2)\n",
      "(1.77079, -8.25296, -0.112758)\n",
      "\n",
      "Value 16: (gtsam::Pose2)\n",
      "(2.1439, -8.32859, -0.087623)\n",
      "\n",
      "Value 17: (gtsam::Pose2)\n",
      "(2.65698, -8.34611, -0.0959091)\n",
      "\n",
      "Value 18: (gtsam::Pose2)\n",
      "(3.12541, -8.42565, -0.0806115)\n",
      "\n",
      "Value 19: (gtsam::Pose2)\n",
      "(3.53363, -8.45891, -0.0845326)\n",
      "\n",
      "Value 20: (gtsam::Pose2)\n",
      "(3.55684, -8.47, 0.034201)\n",
      "\n",
      "Value 21: (gtsam::Pose2)\n",
      "(3.96274, -8.431, 0.140473)\n",
      "\n",
      "Value 22: (gtsam::Pose2)\n",
      "(4.31056, -8.34624, 0.306897)\n",
      "\n",
      "Value 23: (gtsam::Pose2)\n",
      "(4.66862, -8.19135, 0.428578)\n",
      "\n",
      "Value 24: (gtsam::Pose2)\n",
      "(5.03754, -7.96849, 0.602939)\n",
      "\n",
      "Value 25: (gtsam::Pose2)\n",
      "(5.41234, -7.65623, 0.77031)\n",
      "\n",
      "Value 26: (gtsam::Pose2)\n",
      "(5.66194, -7.35412, 0.906803)\n",
      "\n",
      "Value 27: (gtsam::Pose2)\n",
      "(5.92749, -6.99097, 1.0769)\n",
      "\n",
      "Value 28: (gtsam::Pose2)\n",
      "(6.09009, -6.54337, 1.24953)\n",
      "\n",
      "Value 29: (gtsam::Pose2)\n",
      "(6.20229, -6.1034, 1.43874)\n",
      "\n",
      "Value 30: (gtsam::Pose2)\n",
      "(6.24968, -5.69402, 1.61699)\n",
      "\n",
      "Value 31: (gtsam::Pose2)\n",
      "(6.2057, -5.25606, 1.74942)\n",
      "\n",
      "Value 32: (gtsam::Pose2)\n",
      "(6.1721, -4.86009, 1.89654)\n",
      "\n",
      "Value 33: (gtsam::Pose2)\n",
      "(5.96096, -4.53113, 2.04494)\n",
      "\n",
      "Value 34: (gtsam::Pose2)\n",
      "(5.74064, -4.23371, 2.20503)\n",
      "\n",
      "Value 35: (gtsam::Pose2)\n",
      "(5.46392, -3.92463, 2.37666)\n",
      "\n",
      "Value 36: (gtsam::Pose2)\n",
      "(5.13172, -3.69272, 2.5577)\n",
      "\n",
      "Value 37: (gtsam::Pose2)\n",
      "(4.78486, -3.49824, 2.69472)\n",
      "\n",
      "Value 38: (gtsam::Pose2)\n",
      "(4.38187, -3.36749, 2.87959)\n",
      "\n",
      "Value 39: (gtsam::Pose2)\n",
      "(3.95877, -3.2854, 3.00963)\n",
      "\n",
      "Value 40: (gtsam::Pose2)\n",
      "(3.96481, -3.28711, 3.08222)\n",
      "\n",
      "Value 41: (gtsam::Pose2)\n",
      "(3.46369, -3.25009, -3.14)\n",
      "\n",
      "Value 42: (gtsam::Pose2)\n",
      "(2.95083, -3.20042, 3.13016)\n",
      "\n",
      "Value 43: (gtsam::Pose2)\n",
      "(2.49305, -3.22159, -3.11178)\n",
      "\n",
      "Value 44: (gtsam::Pose2)\n",
      "(2.00734, -3.25448, -3.11809)\n",
      "\n",
      "Value 45: (gtsam::Pose2)\n",
      "(1.49646, -3.31848, -3.12857)\n",
      "\n",
      "Value 46: (gtsam::Pose2)\n",
      "(1.03378, -3.34606, -3.11307)\n",
      "\n",
      "Value 47: (gtsam::Pose2)\n",
      "(0.514652, -3.39167, -3.13351)\n",
      "\n",
      "Value 48: (gtsam::Pose2)\n",
      "(0.0738996, -3.42646, 3.13742)\n",
      "\n",
      "Value 49: (gtsam::Pose2)\n",
      "(-0.389728, -3.38948, -3.11192)\n",
      "\n",
      "Value 50: (gtsam::Pose2)\n",
      "(-0.894661, -3.41356, -3.08641)\n",
      "\n",
      "Value 51: (gtsam::Pose2)\n",
      "(-1.37549, -3.40631, -3.11199)\n",
      "\n",
      "Value 52: (gtsam::Pose2)\n",
      "(-1.8424, -3.4114, -3.13872)\n",
      "\n",
      "Value 53: (gtsam::Pose2)\n",
      "(-2.3436, -3.39841, -3.14112)\n",
      "\n",
      "Value 54: (gtsam::Pose2)\n",
      "(-2.86307, -3.4407, -3.09444)\n",
      "\n",
      "Value 55: (gtsam::Pose2)\n",
      "(-3.36566, -3.47079, -3.1097)\n",
      "\n",
      "Value 56: (gtsam::Pose2)\n",
      "(-3.89411, -3.53363, -3.1275)\n",
      "\n",
      "Value 57: (gtsam::Pose2)\n",
      "(-4.41712, -3.53111, -3.13264)\n",
      "\n",
      "Value 58: (gtsam::Pose2)\n",
      "(-4.92273, -3.5236, -3.10578)\n",
      "\n",
      "Value 59: (gtsam::Pose2)\n",
      "(-5.37831, -3.51904, -3.10937)\n",
      "\n",
      "Value 60: (gtsam::Pose2)\n",
      "(-5.34664, -3.52885, -3.07214)\n",
      "\n",
      "Value 61: (gtsam::Pose2)\n",
      "(-5.77966, -3.51566, -2.99382)\n",
      "\n",
      "Value 62: (gtsam::Pose2)\n",
      "(-6.20307, -3.68137, -2.79615)\n",
      "\n",
      "Value 63: (gtsam::Pose2)\n",
      "(-6.63869, -3.87799, -2.56733)\n",
      "\n",
      "Value 64: (gtsam::Pose2)\n",
      "(-6.97676, -4.12995, -2.44044)\n",
      "\n",
      "Value 65: (gtsam::Pose2)\n",
      "(-7.26558, -4.46156, -2.29411)\n",
      "\n",
      "Value 66: (gtsam::Pose2)\n",
      "(-7.52531, -4.78829, -2.13871)\n",
      "\n",
      "Value 67: (gtsam::Pose2)\n",
      "(-7.75021, -5.13142, -1.93193)\n",
      "\n",
      "Value 68: (gtsam::Pose2)\n",
      "(-7.87337, -5.47912, -1.75627)\n",
      "\n",
      "Value 69: (gtsam::Pose2)\n",
      "(-7.92918, -5.93494, -1.59943)\n",
      "\n",
      "Value 70: (gtsam::Pose2)\n",
      "(-7.91387, -6.36499, -1.40778)\n",
      "\n",
      "Value 71: (gtsam::Pose2)\n",
      "(-7.84094, -6.77201, -1.25661)\n",
      "\n",
      "Value 72: (gtsam::Pose2)\n",
      "(-7.639, -7.15939, -1.04327)\n",
      "\n",
      "Value 73: (gtsam::Pose2)\n",
      "(-7.43692, -7.52307, -0.919435)\n",
      "\n",
      "Value 74: (gtsam::Pose2)\n",
      "(-7.12022, -7.89993, -0.74035)\n",
      "\n",
      "Value 75: (gtsam::Pose2)\n",
      "(-6.75168, -8.21629, -0.580841)\n",
      "\n",
      "Value 76: (gtsam::Pose2)\n",
      "(-6.40706, -8.43939, -0.391501)\n",
      "\n",
      "Value 77: (gtsam::Pose2)\n",
      "(-5.98986, -8.63407, -0.196703)\n",
      "\n",
      "Value 78: (gtsam::Pose2)\n",
      "(-5.5691, -8.67614, -0.0254196)\n",
      "\n",
      "Value 79: (gtsam::Pose2)\n",
      "(-5.17981, -8.6599, 0.0275265)\n",
      "\n",
      "Value 80: (gtsam::Pose2)\n",
      "(-5.12418, -8.59276, 0.112721)\n",
      "\n",
      "Value 81: (gtsam::Pose2)\n",
      "(-4.61672, -8.50042, 0.107021)\n",
      "\n",
      "Value 82: (gtsam::Pose2)\n",
      "(-4.02411, -8.37735, 0.157208)\n",
      "\n",
      "Value 83: (gtsam::Pose2)\n",
      "(-3.48792, -8.32267, 0.137317)\n",
      "\n",
      "Value 84: (gtsam::Pose2)\n",
      "(-2.94216, -8.27523, 0.173118)\n",
      "\n",
      "Value 85: (gtsam::Pose2)\n",
      "(-2.38078, -8.1781, 0.161106)\n",
      "\n",
      "Value 86: (gtsam::Pose2)\n",
      "(-1.82531, -8.04593, 0.103212)\n",
      "\n",
      "Value 87: (gtsam::Pose2)\n",
      "(-1.27033, -7.99751, 0.0808851)\n",
      "\n",
      "Value 88: (gtsam::Pose2)\n",
      "(-0.732655, -7.94988, 0.136972)\n",
      "\n",
      "Value 89: (gtsam::Pose2)\n",
      "(-0.18435, -7.88731, 0.100156)\n",
      "\n",
      "Value 90: (gtsam::Pose2)\n",
      "(0.388252, -7.85244, 0.096744)\n",
      "\n",
      "Value 91: (gtsam::Pose2)\n",
      "(0.956821, -7.79569, 0.0851572)\n",
      "\n",
      "Value 92: (gtsam::Pose2)\n",
      "(1.49737, -7.72249, 0.0845681)\n",
      "\n",
      "Value 93: (gtsam::Pose2)\n",
      "(2.05159, -7.66278, 0.0745319)\n",
      "\n",
      "Value 94: (gtsam::Pose2)\n",
      "(2.61049, -7.62938, 0.108547)\n",
      "\n",
      "Value 95: (gtsam::Pose2)\n",
      "(3.17784, -7.61977, 0.102327)\n",
      "\n",
      "Value 96: (gtsam::Pose2)\n",
      "(3.75872, -7.56654, 0.0896511)\n",
      "\n",
      "Value 97: (gtsam::Pose2)\n",
      "(4.33911, -7.54697, 0.0916021)\n",
      "\n",
      "Value 98: (gtsam::Pose2)\n",
      "(4.9021, -7.46959, 0.0606016)\n",
      "\n",
      "Value 99: (gtsam::Pose2)\n",
      "(5.41106, -7.39379, 0.037249)\n",
      "\n",
      "Value 100: (gtsam::Pose2)\n",
      "(5.40564, -7.32101, 0.105965)\n",
      "\n",
      "Value 101: (gtsam::Pose2)\n",
      "(5.77454, -7.1977, 0.219016)\n",
      "\n",
      "Value 102: (gtsam::Pose2)\n",
      "(6.19273, -6.98153, 0.382105)\n",
      "\n",
      "Value 103: (gtsam::Pose2)\n",
      "(6.63147, -6.76513, 0.539183)\n",
      "\n",
      "Value 104: (gtsam::Pose2)\n",
      "(6.99658, -6.47285, 0.743001)\n",
      "\n",
      "Value 105: (gtsam::Pose2)\n",
      "(7.33546, -6.13187, 0.905925)\n",
      "\n",
      "Value 106: (gtsam::Pose2)\n",
      "(7.60215, -5.70238, 1.0742)\n",
      "\n",
      "Value 107: (gtsam::Pose2)\n",
      "(7.76383, -5.23856, 1.27824)\n",
      "\n",
      "Value 108: (gtsam::Pose2)\n",
      "(7.86306, -4.82143, 1.46616)\n",
      "\n",
      "Value 109: (gtsam::Pose2)\n",
      "(7.91225, -4.34378, 1.61785)\n",
      "\n",
      "Value 110: (gtsam::Pose2)\n",
      "(7.8247, -3.90991, 1.75071)\n",
      "\n",
      "Value 111: (gtsam::Pose2)\n",
      "(7.69177, -3.39991, 1.93466)\n",
      "\n",
      "Value 112: (gtsam::Pose2)\n",
      "(7.45775, -2.9419, 2.12266)\n",
      "\n",
      "Value 113: (gtsam::Pose2)\n",
      "(7.18397, -2.59464, 2.25652)\n",
      "\n",
      "Value 114: (gtsam::Pose2)\n",
      "(6.87435, -2.27646, 2.39238)\n",
      "\n",
      "Value 115: (gtsam::Pose2)\n",
      "(6.49221, -1.99015, 2.50214)\n",
      "\n",
      "Value 116: (gtsam::Pose2)\n",
      "(6.11088, -1.71813, 2.68519)\n",
      "\n",
      "Value 117: (gtsam::Pose2)\n",
      "(5.72172, -1.55639, 2.86336)\n",
      "\n",
      "Value 118: (gtsam::Pose2)\n",
      "(5.24048, -1.41036, 3.01859)\n",
      "\n",
      "Value 119: (gtsam::Pose2)\n",
      "(4.80075, -1.42643, -0.0473971)\n",
      "\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(readG2o(filename=\"graph.g2o\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information Matrix from NoiseModels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100.   0.   0.]\n",
      " [  0.   1.   0.]\n",
      " [  0.   0.   0.]]\n"
     ]
    }
   ],
   "source": [
    "print(noiseModel.Diagonal.Sigmas(np.array([0.1, 1.0, 0.0])).information())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128148.36785865757\n",
      "386.5308888762148\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marginals_with_loop = gtsam.Marginals(graph, initial)\n",
    "# print(marginals_with_loop.marginalCovariance(1))\n",
    "print(graph.error(initial))\n",
    "print(graph.error(result))\n",
    "\n",
    "for i in range(1, 120):\n",
    "    gtsam_plot.plot_pose2(0, initial.atPose2(i), 0.1, marginals_without_loop.marginalCovariance(i))\n",
    "for i in range(1, 120):\n",
    "    gtsam_plot.plot_pose2(1, result.atPose2(i), 0.1, marginals_with_loop.marginalCovariance(i))\n",
    "# dir(marginals.marginalCovariance)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}