ukf.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unscented Kalman Filter : Using x and y co-ordinates as measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing csv files and reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_odo = genfromtxt('true_odometry.csv', delimiter=',')\n",
    "sen_odo = genfromtxt('sensor_odom.csv',delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting the data into individual arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sen_pos_x, sen_pos_y = sen_odo[1:,1], sen_odo[1:,2]\n",
    "sen_pos_theta = sen_odo[1:,3]\n",
    "\n",
    "true_x, true_y, true_theta = true_odo[1:,1], true_odo[1:,2], true_odo[1:,3]\n",
    "v, w = true_odo[1:,4], true_odo[1:,5]\n",
    "time = sen_odo[1:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observation that we are making - x and y position\n",
    "z = np.c_[sen_pos_x, sen_pos_y]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining various variables and matrices that we will use in the Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transition Matrix\n",
    "F_t = np.array([  [1, 0, 0],\n",
    "                  [0, 1, 0],\n",
    "                  [0, 0, 1]  ])\n",
    "\n",
    "# Process Covariance\n",
    "Q_t = 0.0044 * np.identity(3)\n",
    "\n",
    "# Measurement Covariance\n",
    "R_t = np.array([    [0.22, 0],\n",
    "                    [0, 0.22]   ])\n",
    "\n",
    "# Measurement Matrix\n",
    "H_t = np.array([    [1, 0, 0],\n",
    "                    [0, 1, 0]   ])\n",
    "\n",
    "# Find lambda for any of the weights\n",
    "gamma = (0.44)**2\n",
    "beta = 2\n",
    "\n",
    "# Defining alpha (weight) values for mean - this step is specific to UKF\n",
    "\n",
    "alpha_0 = 1/2\n",
    "\n",
    "alpha_1 = 1/12\n",
    "alpha_2 = 1/12\n",
    "alpha_3 = 1/12\n",
    "alpha_4 = 1/12\n",
    "alpha_5 = 1/12\n",
    "alpha_6 = 1/12\n",
    "\n",
    "cov_alpha_0 = alpha_0 + (1 - gamma + beta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining Two Functions that will be used in the main loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing the Cholesky Decomposition\n",
    "def decompose(P):\n",
    "\n",
    "    L  = np.linalg.cholesky(P)\n",
    "\n",
    "    L_1 = np.array([    [ L[0,0] ],\n",
    "                        [ L[1,0] ],\n",
    "                        [ L[2,0] ]   ])\n",
    "\n",
    "    L_2 = np.array([    [ L[0,1] ],\n",
    "                        [ L[1,1] ],\n",
    "                        [ L[2,1] ]   ])\n",
    "\n",
    "    L_3 = np.array([    [ L[0,2] ],\n",
    "                        [ L[1,2] ],\n",
    "                        [ L[2,2] ]   ])\n",
    "\n",
    "    return L_1, L_2, L_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigma_points(mean,L_1,L_2,L_3):\n",
    "    # Calculating Sigma Points\n",
    "    sig_0 = mean\n",
    "\n",
    "    sig_1 = mean + math.sqrt(3) * L_1\n",
    "    sig_2 = mean + math.sqrt(3) * L_2\n",
    "    sig_3 = mean + math.sqrt(3) * L_3\n",
    "\n",
    "    sig_4 = mean - math.sqrt(3) * L_1\n",
    "    sig_5 = mean - math.sqrt(3) * L_2\n",
    "    sig_6 = mean - math.sqrt(3) * L_3\n",
    "\n",
    "    return sig_0, sig_1, sig_2, sig_3, sig_4, sig_5, sig_6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initial Mean and Cov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean \n",
    "mean = np.array([       [sen_pos_x[0]],\n",
    "                        [sen_pos_y[0]],\n",
    "                        [sen_pos_theta[0]]      ])\n",
    "\n",
    "# Cov             \n",
    "P = np.array([    [0.44, 0, 0],\n",
    "                  [0, 0.44, 0], \n",
    "                  [0, 0, 0.44]   ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining empty lists which will be used for plotting purposes later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "kal_x, kal_y, kal_theta = [], [], []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Kalman Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(2113):\n",
    "    \n",
    "    if i > 0:\n",
    "        dt = time[i] - time[i-1]\n",
    "    else:\n",
    "        dt = 0\n",
    "\n",
    "    # Prediction Step\n",
    "\n",
    "    L_1, L_2, L_3 = decompose(P)\n",
    "\n",
    "    sig_0, sig_1, sig_2, sig_3, sig_4, sig_5, sig_6 = sigma_points(mean,L_1,L_2,L_3)\n",
    "\n",
    "\n",
    "    # Input Transition Matrix - B\n",
    "    B_t = np.array([    [dt * (math.cos(sen_pos_theta[i])), 0],\n",
    "                        [dt * (math.sin(sen_pos_theta[i])), 0],\n",
    "                        [0, dt]                                    ])\n",
    "    # Input to the system - v and w ( velocity and turning rate )\n",
    "    U_t = np.array([    [v[i]],\n",
    "                        [w[i]]     ])\n",
    "\n",
    "    # Calculating Projected sigma points through Non-linear Motion Model\n",
    "\n",
    "    p_sig_0 = F_t.dot(sig_0) + B_t.dot(U_t)\n",
    "\n",
    "    p_sig_1 = F_t.dot(sig_1) + B_t.dot(U_t)\n",
    "    p_sig_2 = F_t.dot(sig_2) + B_t.dot(U_t)\n",
    "    p_sig_3 = F_t.dot(sig_3) + B_t.dot(U_t)\n",
    "\n",
    "    p_sig_4 = F_t.dot(sig_4) + B_t.dot(U_t)\n",
    "    p_sig_5 = F_t.dot(sig_5) + B_t.dot(U_t)\n",
    "    p_sig_6 = F_t.dot(sig_6) + B_t.dot(U_t)\n",
    "\n",
    "\n",
    "    # Calculating Prediction Mean : Summation of (alphs * p_sig_points)\n",
    "    predict_mean = (alpha_0*p_sig_0) + (alpha_1*p_sig_1) + (alpha_2*p_sig_2) + (alpha_3*p_sig_3) + (alpha_4*p_sig_4) + (alpha_5*p_sig_5) + (alpha_6*p_sig_6)\n",
    "\n",
    "    # Calculating Prediction Cov : \n",
    "    cov_0 = cov_alpha_0 * ( (p_sig_0 - predict_mean).dot((p_sig_0 - predict_mean).T) ) \n",
    "\n",
    "    cov_1 = alpha_1 * ( (p_sig_1 - predict_mean).dot((p_sig_1 - predict_mean).T) )\n",
    "    cov_2 = alpha_2 * ( (p_sig_2 - predict_mean).dot((p_sig_2 - predict_mean).T) ) \n",
    "    cov_3 = alpha_3 * ( (p_sig_3 - predict_mean).dot((p_sig_3 - predict_mean).T) ) \n",
    "\n",
    "    cov_4 = alpha_4 * ( (p_sig_4 - predict_mean).dot((p_sig_4 - predict_mean).T) ) \n",
    "    cov_5 = alpha_5 * ( (p_sig_5 - predict_mean).dot((p_sig_5 - predict_mean).T) ) \n",
    "    cov_6 = alpha_6 * ( (p_sig_6 - predict_mean).dot((p_sig_6 - predict_mean).T) ) \n",
    "\n",
    "\n",
    "    predict_cov = cov_0 + cov_1 + cov_2 + cov_3 + cov_4 + cov_5 + cov_6 + Q_t\n",
    "\n",
    "\n",
    "    # Update Step\n",
    "    L_1, L_2, L_3 = decompose(predict_cov)\n",
    "\n",
    "    sig_0, sig_1, sig_2, sig_3, sig_4, sig_5, sig_6 = sigma_points(predict_mean,L_1,L_2,L_3)\n",
    "    \n",
    "    # Calculating Projected sigma points through Measurement Model\n",
    "\n",
    "    m_sig_0 = H_t.dot(sig_0) \n",
    "\n",
    "    m_sig_1 = H_t.dot(sig_1) \n",
    "    m_sig_2 = H_t.dot(sig_2) \n",
    "    m_sig_3 = H_t.dot(sig_3) \n",
    "\n",
    "    m_sig_4 = H_t.dot(sig_4) \n",
    "    m_sig_5 = H_t.dot(sig_5) \n",
    "    m_sig_6 = H_t.dot(sig_6) \n",
    "\n",
    "    # Calculating Measurement Mean : Summation of (alphs * p_sig_points)\n",
    "    measure_mean_y = (alpha_0*m_sig_0) + (alpha_1*m_sig_1) + (alpha_2*m_sig_2) + (alpha_3*m_sig_3) + (alpha_4*m_sig_4) + (alpha_5*m_sig_5) + (alpha_6*m_sig_6)\n",
    "    \n",
    "    # Calculating Measurement Cov :\n",
    "\n",
    "    m_cov_0 = cov_alpha_0 * ( (m_sig_0 - measure_mean_y).dot((m_sig_0 - measure_mean_y).T) )\n",
    "\n",
    "    m_cov_1 = alpha_1 * ( (m_sig_1 - measure_mean_y).dot((m_sig_1 - measure_mean_y).T) ) \n",
    "    m_cov_2 = alpha_2 * ( (m_sig_2 - measure_mean_y).dot((m_sig_2 - measure_mean_y).T) ) \n",
    "    m_cov_3 = alpha_3 * ( (m_sig_3 - measure_mean_y).dot((m_sig_3 - measure_mean_y).T) ) \n",
    "\n",
    "    m_cov_4 = alpha_4 * ( (m_sig_4 - measure_mean_y).dot((m_sig_4 - measure_mean_y).T) ) \n",
    "    m_cov_5 = alpha_5 * ( (m_sig_5 - measure_mean_y).dot((m_sig_5 - measure_mean_y).T) ) \n",
    "    m_cov_6 = alpha_6 * ( (m_sig_6 - measure_mean_y).dot((m_sig_6 - measure_mean_y).T) ) \n",
    "\n",
    "    predict_cov_y = (m_cov_0 + m_cov_1 + m_cov_2 + m_cov_3 + m_cov_4 + m_cov_5 + m_cov_6) + R_t\n",
    "    S = np.linalg.inv(predict_cov_y)\n",
    "\n",
    "\n",
    "    # Calculating Kalman Gain\n",
    "    cov_xy_0 = cov_alpha_0 * ( (sig_0 - predict_mean).dot((m_sig_0 - measure_mean_y).T) )\n",
    "\n",
    "    cov_xy_1 = alpha_1 * ( (sig_1 - predict_mean).dot((m_sig_1 - measure_mean_y).T) )\n",
    "    cov_xy_2 = alpha_2 * ( (sig_2 - predict_mean).dot((m_sig_2 - measure_mean_y).T) )\n",
    "    cov_xy_3 = alpha_3 * ( (sig_3 - predict_mean).dot((m_sig_3 - measure_mean_y).T) )\n",
    "\n",
    "    cov_xy_4 = alpha_4 * ( (sig_4 - predict_mean).dot((m_sig_4 - measure_mean_y).T) )\n",
    "    cov_xy_5 = alpha_5 * ( (sig_5 - predict_mean).dot((m_sig_5 - measure_mean_y).T) )\n",
    "    cov_xy_6 = alpha_6 * ( (sig_6 - predict_mean).dot((m_sig_6 - measure_mean_y).T) )\n",
    "\n",
    "    cov_xy = ( cov_xy_0 + cov_xy_1 + cov_xy_2 + cov_xy_3 + cov_xy_4 + cov_xy_5 + cov_xy_6 )\n",
    "\n",
    "    K = cov_xy.dot(S)\n",
    "\n",
    "\n",
    "    # Reshaping the measurement data\n",
    "    Z_t = z[i].transpose()\n",
    "    Z_t = Z_t.reshape(Z_t.shape[0], -1)\n",
    "\n",
    "    # Corrected Mean and Cov\n",
    "\n",
    "    mean = predict_mean + K.dot(Z_t - measure_mean_y)\n",
    "\n",
    "    P = predict_cov - K.dot(predict_cov_y).dot(K.T)\n",
    "\n",
    "    kal_x.append(mean[0])\n",
    "    kal_y.append(mean[1])\n",
    "    kal_theta.append(mean[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n\n******************************\n\n Final Filter State Matrix : \n [[-1.90937007]\n [-0.26245011]\n [ 6.23899338]] \n\n"
    }
   ],
   "source": [
    "print('\\n')\n",
    "print('*'*30)\n",
    "print('\\n',\"Final Filter State Matrix : \\n\", mean,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Plotting Purposes\n",
    "kal_x = np.concatenate(kal_x).ravel()\n",
    "kal_y = np.concatenate(kal_y).ravel()\n",
    "kal_theta = np.concatenate(kal_theta).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
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     "metadata": {},
     "execution_count": 14
    },
    {
     "output_type": "display_data",
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180.954632 \nL 245.725843 177.354953 \nL 246.884488 172.577529 \nL 247.169841 172.077005 \nL 247.455177 172.694795 \nL 248.61384 167.744469 \nL 248.899176 167.343105 \nL 249.17587 166.603887 \nL 249.478507 166.347469 \nL 250.049179 165.307002 \nL 250.343174 164.093553 \nL 250.913846 162.620576 \nL 251.493177 160.437998 \nL 251.778531 160.172446 \nL 252.072509 159.660345 \nL 252.366504 158.550079 \nL 252.643198 158.235466 \nL 252.928534 156.711489 \nL 253.801843 154.04339 \nL 254.078537 152.310146 \nL 254.372533 152.077653 \nL 254.666528 152.143535 \nL 254.951864 151.528533 \nL 255.228558 150.499135 \nL 255.531195 149.893057 \nL 256.387203 147.686175 \nL 256.681198 145.982283 \nL 256.966534 145.162621 \nL 257.26053 143.513731 \nL 257.545866 143.166147 \nL 257.82256 143.060385 \nL 259.292501 137.667593 \nL 259.854531 135.985953 \nL 260.139885 135.52267 \nL 260.719199 133.322104 \nL 261.004552 132.852644 \nL 262.154555 130.15619 \nL 262.733887 127.656353 \nL 263.019223 127.08604 \nL 263.598554 125.427932 \nL 263.901191 124.942254 \nL 264.463221 122.510472 \nL 264.791801 121.803214 \nL 265.025251 120.878762 \nL 265.327888 120.565149 \nL 266.183914 118.574143 \nL 267.342576 115.213667 \nL 267.636554 114.14794 \nL 267.921908 113.840488 \nL 268.207244 112.277621 \nL 268.501239 112.244907 \nL 268.786575 112.026538 \nL 269.936578 109.276097 \nL 270.230573 109.011426 \nL 270.507268 108.590125 \nL 271.095241 106.91637 \nL 271.380577 106.305775 \nL 271.959908 104.071957 \nL 272.245244 103.283831 \nL 272.530597 102.943305 \nL 272.824575 102.038592 \nL 273.395265 101.13721 \nL 273.706543 100.365472 \nL 274.259932 97.850004 \nL 274.55391 96.791472 \nL 275.124599 95.647683 \nL 275.70393 93.954844 \nL 275.989266 93.613196 \nL 276.568598 92.318522 \nL 277.147929 89.874123 \nL 277.415964 89.606903 \nL 277.718601 88.523175 \nL 278.012596 88.952527 \nL 278.877263 84.884263 \nL 279.162599 84.713334 \nL 279.447953 83.154384 \nL 280.31262 80.840127 \nL 280.606598 79.35766 \nL 280.891951 78.890382 \nL 281.177287 77.861687 \nL 281.471265 77.226099 \nL 282.041955 74.552317 \nL 282.621286 73.166456 \nL 282.906622 71.810772 \nL 283.485953 71.176081 \nL 283.771289 70.989889 \nL 284.35062 68.698544 \nL 284.929952 67.385374 \nL 285.223929 67.14709 \nL 285.500624 66.095619 \nL 285.794619 65.628396 \nL 286.079955 65.547074 \nL 286.365308 64.555815 \nL 286.659286 64.194295 \nL 287.523953 61.828197 \nL 287.809307 61.848019 \nL 288.388621 60.173489 \nL 288.673974 59.61937 \nL 288.95931 59.328945 \nL 289.253288 58.793761 \nL 289.538641 57.924779 \nL 289.823977 57.689017 \nL 290.403309 56.114136 \nL 290.688645 55.730459 \nL 290.98264 55.913252 \nL 291.553312 54.53333 \nL 291.847307 54.442048 \nL 292.141285 54.078472 \nL 292.417979 54.051277 \nL 292.99731 53.695691 \nL 293.568 52.088911 \nL 293.861978 51.581403 \nL 294.147331 50.529072 \nL 294.441309 50.245487 \nL 295.003357 50.085007 \nL 295.305976 49.771759 \nL 295.59133 49.025639 \nL 295.876666 48.96342 \nL 296.732691 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325.275422 53.012894 \nL 325.569417 54.42292 \nL 325.846112 54.737786 \nL 326.710779 56.354275 \nL 327.048 56.016645 \nL 327.307394 56.649472 \nL 327.575446 56.842867 \nL 327.860782 56.481972 \nL 328.440113 58.49185 \nL 329.028086 60.286968 \nL 329.598776 61.413581 \nL 329.884112 61.991314 \nL 330.178107 61.7356 \nL 330.757421 61.986663 \nL 331.025473 61.985715 \nL 331.32811 62.468698 \nL 331.907442 62.023537 \nL 332.192778 62.699318 \nL 332.495415 62.988491 \nL 332.772109 63.046539 \nL 333.057445 62.859501 \nL 333.35144 63.545972 \nL 333.636776 63.722722 \nL 333.922112 63.60915 \nL 334.216107 64.293268 \nL 334.786797 65.062042 \nL 336.230795 63.664791 \nL 336.810109 64.208643 \nL 337.095463 63.209958 \nL 337.372157 63.114026 \nL 337.674794 63.418078 \nL 337.96013 63.254464 \nL 338.245466 62.40332 \nL 338.539461 62.253867 \nL 338.824797 62.531833 \nL 339.110133 63.176076 \nL 339.689464 63.164135 \nL 340.268796 63.718145 \nL 341.133463 63.548619 \nL 341.418799 63.224187 \nL 341.704152 62.230614 \nL 342.283466 62.643239 \nL 342.56882 62.034291 \nL 342.862797 61.773111 \nL 343.148151 62.22471 \nL 343.433487 61.687988 \nL 343.727465 62.022535 \nL 344.298154 62.969047 \nL 344.592132 62.960661 \nL 344.877485 63.546943 \nL 345.162821 63.662303 \nL 345.456817 63.447096 \nL 345.742153 64.142681 \nL 346.321484 63.9492 \nL 346.60682 64.220892 \nL 346.892156 64.108437 \nL 347.186151 63.83537 \nL 347.471487 63.359918 \nL 347.756823 63.788721 \nL 348.050818 64.029373 \nL 348.621508 63.326554 \nL 348.915486 63.078159 \nL 349.200822 64.168187 \nL 349.486175 63.845756 \nL 349.780153 63.770506 \nL 350.065506 64.451827 \nL 350.3249 64.555159 \nL 350.627537 64.924522 \nL 350.930174 65.93892 \nL 351.206868 65.729587 \nL 351.500846 65.967633 \nL 351.794841 65.891016 \nL 352.071535 66.208143 \nL 352.374172 66.109389 \nL 352.659508 66.498323 \nL 353.238839 66.812986 \nL 353.524175 66.109734 \nL 353.80087 66.810275 \nL 354.103507 67.212515 \nL 354.380201 67.363372 \nL 354.674179 67.303436 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456.134487 345.091159 \nL 457.284508 348.149014 \nL 457.56122 349.103644 \nL 458.149175 351.354113 \nL 458.434529 352.251728 \nL 458.728524 352.622073 \nL 459.299196 354.552198 \nL 459.593191 355.623772 \nL 460.163863 356.740797 \nL 460.457858 357.287802 \nL 461.322526 360.217968 \nL 461.599202 360.362117 \nL 462.187193 361.413852 \nL 462.757865 363.428542 \nL 463.05186 364.079068 \nL 463.337178 364.438597 \nL 463.622532 364.456952 \nL 464.201881 364.812576 \nL 464.487199 366.171143 \nL 464.781195 366.773981 \nL 465.066548 366.925407 \nL 465.351866 367.291363 \nL 465.931215 368.2571 \nL 466.216534 368.488883 \nL 467.66055 372.047949 \nL 467.937226 372.345275 \nL 468.239863 372.479332 \nL 468.525217 373.150519 \nL 468.810535 374.537766 \nL 469.104531 375.006435 \nL 469.969198 378.032253 \nL 470.254552 378.606543 \nL 470.833865 379.135051 \nL 471.689891 381.739941 \nL 471.983886 381.78385 \nL 472.5632 382.738377 \nL 472.83127 382.87166 \nL 473.125265 383.435856 \nL 473.427902 384.779038 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(1, figsize=(12,8))\n",
    "plt.title('Estimated (Kalman) Pos X vs True Pos X', fontweight='bold')\n",
    "plt.xlabel('Time') \n",
    "plt.ylabel('X Position Estimate')  \n",
    "plt.plot(time,kal_x,'-', linewidth=3,label='Estimated x Value')\n",
    "plt.plot(time,true_x, linewidth=3,label='True x Value')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x107ee69d0>"
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
     "data": {
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427.998317 \nL 242.846487 428.134703 \nL 243.131823 428.463236 \nL 243.425819 429.337185 \nL 243.711155 429.784048 \nL 244.290486 431.150488 \nL 244.861175 431.875095 \nL 245.155153 431.915963 \nL 245.440489 432.077044 \nL 246.01982 430.4633 \nL 246.305174 430.721481 \nL 246.59051 430.362096 \nL 246.884488 431.03153 \nL 247.169841 431.277326 \nL 247.455177 430.838333 \nL 247.749155 431.462635 \nL 248.034508 431.580742 \nL 248.319844 432.05001 \nL 248.61384 430.950919 \nL 248.899176 430.492214 \nL 249.17587 430.599549 \nL 249.763843 432.004183 \nL 250.049179 431.660202 \nL 250.343174 431.561292 \nL 250.913846 432.342703 \nL 251.207841 432.971827 \nL 251.493177 432.88455 \nL 251.778531 431.91593 \nL 252.072509 431.964474 \nL 252.366504 432.736687 \nL 252.643198 431.549328 \nL 252.928534 431.112436 \nL 253.222529 431.174899 \nL 253.507865 431.90636 \nL 253.801843 431.783512 \nL 254.078537 431.285754 \nL 254.372533 432.100242 \nL 254.666528 431.838424 \nL 254.951864 431.877494 \nL 255.228558 431.705542 \nL 255.79923 431.004397 \nL 256.101867 430.192845 \nL 256.387203 430.727753 \nL 256.681198 430.743969 \nL 256.966534 430.238555 \nL 257.26053 430.45728 \nL 257.545866 431.631406 \nL 257.82256 430.411736 \nL 258.125197 430.412827 \nL 258.410533 430.234129 \nL 258.695886 428.441015 \nL 258.989864 428.16468 \nL 259.560554 428.036282 \nL 260.139885 429.229249 \nL 260.425221 431.013668 \nL 260.719199 431.599478 \nL 261.004552 432.887955 \nL 261.289888 433.088452 \nL 261.583866 434.263186 \nL 261.869219 434.245057 \nL 262.154555 432.831577 \nL 262.448551 432.833457 \nL 262.733887 433.252671 \nL 263.019223 433.048313 \nL 263.313218 431.273141 \nL 264.177885 432.71078 \nL 264.463221 433.27758 \nL 264.791801 433.156407 \nL 265.025251 433.388083 \nL 265.898578 432.646525 \nL 266.183914 433.450059 \nL 266.771887 433.58852 \nL 267.05724 432.393595 \nL 267.342576 432.844089 \nL 267.636554 431.218825 \nL 267.921908 431.349742 \nL 268.207244 431.304078 \nL 268.501239 430.951743 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417.662046 \nL 282.041955 416.511088 \nL 282.33595 416.935716 \nL 282.621286 416.777798 \nL 283.485953 413.810145 \nL 283.771289 413.986316 \nL 284.35062 413.508347 \nL 284.635956 412.628232 \nL 284.929952 413.039897 \nL 285.223929 412.729802 \nL 285.500624 411.96506 \nL 286.079955 408.859318 \nL 286.659286 407.452299 \nL 287.229976 405.491655 \nL 288.094643 403.015775 \nL 288.95931 398.237741 \nL 289.538641 396.946837 \nL 289.823977 396.522388 \nL 290.117973 394.51278 \nL 290.403309 393.976648 \nL 290.98264 390.931918 \nL 291.267976 389.733916 \nL 291.553312 387.453079 \nL 292.417979 384.170434 \nL 292.711974 381.93722 \nL 293.282664 379.129151 \nL 294.147331 373.063026 \nL 294.441309 373.297497 \nL 294.726662 372.992731 \nL 295.59133 369.893837 \nL 295.876666 369.641125 \nL 296.170661 369.191911 \nL 297.035328 366.680635 \nL 297.37255 364.885584 \nL 297.631943 364.810654 \nL 298.228558 362.61014 \nL 298.470667 361.712678 \nL 299.049999 358.497233 \nL 299.62933 356.737123 \nL 299.914666 356.077384 \nL 300.200019 355.031794 \nL 300.493997 352.743143 \nL 300.779333 352.287237 \nL 301.064687 350.475424 \nL 301.644018 348.398227 \nL 302.223332 344.626543 \nL 302.794021 343.227302 \nL 303.373352 341.083869 \nL 303.952684 338.098803 \nL 304.808692 334.101472 \nL 305.379381 331.566272 \nL 306.25269 328.398415 \nL 307.696689 321.886945 \nL 309.140687 312.077421 \nL 309.426041 309.858236 \nL 310.005372 307.555244 \nL 310.29935 306.146365 \nL 310.576044 305.479992 \nL 310.870039 303.320094 \nL 311.734706 299.574071 \nL 312.020042 299.517686 \nL 312.599374 297.098465 \nL 312.88471 294.978107 \nL 313.464041 292.615291 \nL 314.052014 287.859168 \nL 314.328708 287.4461 \nL 314.614044 285.567533 \nL 314.899398 284.602479 \nL 315.193375 282.722875 \nL 316.032117 280.384587 \nL 317.208063 274.205658 \nL 317.787395 271.668515 \nL 318.072731 270.920682 \nL 318.427236 269.403763 \nL 318.652062 267.632255 \nL 319.802065 263.387577 \nL 320.934785 256.244819 \nL 321.246064 255.345625 \nL 321.522758 253.769074 \nL 321.816753 253.7058 \nL 323.546088 242.730441 \nL 323.840083 242.242868 \nL 324.696091 236.510971 \nL 325.569417 234.095073 \nL 325.846112 233.281487 \nL 326.434085 229.379416 \nL 327.307394 226.85631 \nL 327.575446 226.360053 \nL 328.163419 222.364895 \nL 328.734109 219.863684 \nL 329.028086 217.428371 \nL 329.31344 217.725681 \nL 329.598776 217.616875 \nL 330.178107 216.5936 \nL 331.622106 207.747786 \nL 332.192778 204.445989 \nL 332.495415 202.225852 \nL 333.057445 197.100401 \nL 333.636776 193.230612 \nL 333.922112 192.459549 \nL 334.501443 189.973079 \nL 334.786797 186.788461 \nL 335.651464 182.591569 \nL 335.945442 181.77239 \nL 336.810109 177.054492 \nL 337.095463 176.405699 \nL 337.674794 173.619729 \nL 337.96013 172.86628 \nL 338.539461 169.069357 \nL 339.110133 165.366228 \nL 339.421412 164.048436 \nL 339.992101 160.782374 \nL 340.554132 159.765762 \nL 340.839468 158.705851 \nL 341.133463 157.10773 \nL 341.418799 156.212423 \nL 341.704152 154.287244 \nL 341.99813 153.596922 \nL 342.283466 151.045497 \nL 342.56882 150.72666 \nL 342.862797 150.711757 \nL 343.148151 150.027798 \nL 343.727465 147.277488 \nL 344.012818 147.293673 \nL 344.298154 145.988901 \nL 344.592132 143.413044 \nL 345.162821 141.652296 \nL 345.742153 139.586254 \nL 346.321484 137.599021 \nL 346.892156 134.543821 \nL 347.186151 133.14892 \nL 347.471487 132.584684 \nL 347.756823 130.891284 \nL 348.050818 130.308922 \nL 348.621508 128.41002 \nL 349.200822 125.718949 \nL 349.486175 124.373864 \nL 349.780153 122.194682 \nL 350.065506 122.109951 \nL 350.3249 121.819493 \nL 350.930174 119.399942 \nL 351.206868 117.121178 \nL 351.500846 115.981624 \nL 351.794841 113.884007 \nL 352.071535 112.830543 \nL 352.374172 110.893118 \nL 352.659508 109.836613 \nL 353.238839 106.144625 \nL 355.538863 94.693249 \nL 355.832841 92.528842 \nL 356.118177 91.298548 \nL 356.403531 90.879668 \nL 356.982862 88.670056 \nL 357.259556 86.837818 \nL 357.562176 86.903477 \nL 357.847529 86.120279 \nL 358.124223 86.202447 \nL 358.426843 85.571714 \nL 359.282868 81.530408 \nL 359.576864 80.556625 \nL 360.156195 77.906738 \nL 360.441531 78.023333 \nL 360.726867 76.106422 \nL 361.020862 74.979438 \nL 361.306198 75.476476 \nL 361.591534 74.406726 \nL 361.885529 74.071982 \nL 362.170865 73.469436 \nL 362.456219 72.077445 \nL 362.750197 71.380665 \nL 363.640807 65.501439 \nL 363.900217 64.510832 \nL 364.176912 64.698249 \nL 364.505474 64.153933 \nL 364.773526 63.151891 \nL 365.041579 63.363862 \nL 365.344216 63.304825 \nL 365.620892 64.48374 \nL 365.906246 64.331176 \nL 366.200224 64.661882 \nL 366.494219 63.690741 \nL 366.788197 63.828737 \nL 367.07355 64.819304 \nL 367.358886 64.531757 \nL 367.644222 63.825266 \nL 368.214912 62.085589 \nL 368.508889 62.416611 \nL 368.802885 61.429658 \nL 369.373574 60.54358 \nL 369.667552 60.446116 \nL 369.952888 59.790683 \nL 370.238242 60.383163 \nL 370.532219 59.958935 \nL 370.817573 58.887687 \nL 371.396887 58.189758 \nL 371.68224 57.875968 \nL 371.984877 57.935776 \nL 372.287497 58.767968 \nL 372.546907 58.790742 \nL 372.832243 59.199285 \nL 373.411575 58.887871 \nL 373.990906 60.101363 \nL 374.276242 59.898853 \nL 374.561578 60.185989 \nL 374.855573 61.403907 \nL 375.140909 59.612301 \nL 375.426245 59.915115 \nL 375.711599 59.144879 \nL 375.996935 58.928784 \nL 376.29093 59.290593 \nL 376.584908 58.530056 \nL 376.870244 58.381296 \nL 377.155597 59.063234 \nL 377.734911 58.759092 \nL 378.020264 59.12513 \nL 378.31426 59.013525 \nL 378.599578 57.855192 \nL 378.884932 58.1396 \nL 379.178927 58.038886 \nL 379.464245 58.940753 \nL 379.749599 58.555843 \nL 380.043594 57.816108 \nL 380.328912 59.086318 \nL 380.908261 60.34239 \nL 381.193615 60.385691 \nL 381.772929 59.481215 \nL 382.058282 59.498059 \nL 383.208268 57.456294 \nL 383.519546 56.656119 \nL 383.787617 56.660795 \nL 384.072935 56.249729 \nL 384.349647 56.377691 \nL 384.652284 54.87298 \nL 384.937602 54.353451 \nL 385.231598 54.192384 \nL 385.516951 55.106517 \nL 385.802269 55.512465 \nL 386.096265 55.729587 \nL 386.381618 55.38146 \nL 386.666972 56.419145 \nL 386.960932 55.574738 \nL 387.246286 55.847993 \nL 387.531639 55.780259 \nL 387.825599 56.662132 \nL 388.110953 56.096651 \nL 388.396306 57.320892 \nL 388.690266 56.909162 \nL 388.97562 57.377222 \nL 389.260974 57.365553 \nL 389.554934 58.051564 \nL 389.848929 58.37264 \nL 390.125641 58.083329 \nL 390.419636 58.721839 \nL 390.704955 58.133894 \nL 390.990308 58.651241 \nL 391.275626 58.017886 \nL 391.854975 58.203755 \nL 392.148971 57.620935 \nL 393.013638 56.792259 \nL 393.298956 56.834719 \nL 393.58431 56.356279 \nL 393.878305 56.431986 \nL 394.154982 56.188055 \nL 394.448977 57.545138 \nL 394.742972 57.424346 \nL 395.028326 57.620083 \nL 395.313644 56.953748 \nL 395.624923 57.661022 \nL 396.195595 55.47519 \nL 396.455023 57.152697 \nL 397.336974 55.581512 \nL 397.622328 55.498627 \nL 397.907646 55.884074 \nL 398.193 55.787202 \nL 398.486995 55.356283 \nL 398.772313 54.176285 \nL 399.066309 54.070841 \nL 399.930976 51.121079 \nL 400.216329 52.097511 \nL 400.501683 51.630564 \nL 400.795643 51.467574 \nL 401.080997 51.611074 \nL 401.34039 51.196377 \nL 401.66031 51.369653 \nL 402.231017 50.59307 \nL 402.524977 50.910338 \nL 402.810331 50.874484 \nL 403.087043 50.41535 \nL 403.38968 50.343938 \nL 403.674998 49.830485 \nL 403.960352 50.191416 \nL 404.825019 48.178305 \nL 405.119014 48.407337 \nL 405.404333 47.642581 \nL 405.689686 48.244841 \nL 405.97504 47.736143 \nL 406.269 47.577724 \nL 406.545712 48.770586 \nL 406.848349 48.458245 \nL 407.133667 47.888031 \nL 407.427663 46.357674 \nL 407.713016 46.449141 \nL 407.998334 45.944974 \nL 408.249121 44.980265 \nL 408.577683 44.449344 \nL 408.85436 43.456473 \nL 409.148355 43.734463 \nL 409.442351 44.177495 \nL 409.727704 44.085691 \nL 410.013022 44.757129 \nL 410.307018 44.862304 \nL 410.592371 45.130535 \nL 410.87769 44.548508 \nL 411.742357 43.715369 \nL 412.036352 44.594746 \nL 412.909661 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427.600398 49.528327 \nL 427.885751 49.442785 \nL 428.750419 49.511631 \nL 429.027095 50.118236 \nL 429.329732 48.217995 \nL 429.615086 48.140568 \nL 430.194399 48.374268 \nL 430.765107 48.276621 \nL 431.059102 47.836577 \nL 431.34442 46.401118 \nL 431.629774 46.51967 \nL 431.923769 46.815249 \nL 432.209087 45.19655 \nL 432.494441 45.289287 \nL 433.073755 44.913826 \nL 433.359108 43.766422 \nL 433.653104 43.819418 \nL 434.223776 45.520388 \nL 434.517771 46.215636 \nL 435.088443 46.175527 \nL 435.382438 45.822662 \nL 435.667756 46.351954 \nL 435.95311 46.346561 \nL 436.532459 44.228104 \nL 436.817777 44.431224 \nL 437.111773 43.962495 \nL 437.397126 44.05834 \nL 437.682444 44.701573 \nL 437.97644 44.554884 \nL 438.261793 43.126786 \nL 438.547112 42.62468 \nL 438.841107 43.595691 \nL 439.126461 43.504703 \nL 439.411779 44.030537 \nL 439.705774 43.239598 \nL 439.991128 43.145888 \nL 440.570442 42.085398 \nL 440.855795 42.927042 \nL 441.141113 43.205139 \nL 441.435109 43.071363 \nL 441.720462 43.314334 \nL 442.005816 44.076775 \nL 442.299776 44.01243 \nL 442.58513 45.567153 \nL 443.164443 45.647191 \nL 443.449797 46.409923 \nL 443.73515 47.513835 \nL 444.02911 47.841379 \nL 444.314464 47.632303 \nL 444.599818 47.813425 \nL 445.179131 49.479253 \nL 445.464485 50.729287 \nL 445.75848 50.91079 \nL 446.043798 50.514006 \nL 446.32051 51.510612 \nL 446.631789 50.109228 \nL 446.908466 50.404686 \nL 447.193819 51.089461 \nL 447.487815 51.363627 \nL 448.058487 50.770584 \nL 448.352482 51.530492 \nL 448.923154 54.599825 \nL 449.251716 54.566037 \nL 449.787821 55.350974 \nL 450.937842 59.774559 \nL 451.22316 59.458225 \nL 451.499872 59.417665 \nL 451.811151 58.986583 \nL 452.096504 59.13906 \nL 452.373181 58.406766 \nL 452.641216 58.726753 \nL 452.961172 60.155784 \nL 453.24649 60.895759 \nL 453.540485 62.373316 \nL 453.825839 62.756186 \nL 454.111157 63.325939 \nL 454.422436 62.968372 \nL 454.690506 64.038041 \nL 454.975824 64.361246 \nL 455.26982 64.391061 \nL 455.555173 64.853819 \nL 455.840527 64.87713 \nL 456.134487 65.640918 \nL 456.419841 66.716436 \nL 456.705194 68.460328 \nL 457.56122 70.6571 \nL 457.872499 70.906562 \nL 458.728524 73.636641 \nL 459.005201 73.166762 \nL 459.593191 74.240931 \nL 459.878509 73.948832 \nL 460.457858 75.877777 \nL 460.751818 77.244431 \nL 461.02853 77.963539 \nL 461.322526 77.879421 \nL 461.599202 78.503637 \nL 461.893198 79.532783 \nL 462.187193 79.866418 \nL 462.472511 80.597411 \nL 462.757865 80.673351 \nL 463.337178 82.94528 \nL 463.622532 82.746612 \nL 463.916527 83.288867 \nL 464.487199 84.967169 \nL 465.645862 89.228432 \nL 466.216534 89.175294 \nL 466.510529 90.332658 \nL 466.795883 90.740446 \nL 467.081201 91.496784 \nL 467.375196 91.827858 \nL 467.66055 92.515551 \nL 467.937226 93.66452 \nL 468.239863 96.003936 \nL 468.525217 95.242418 \nL 468.810535 95.273407 \nL 469.104531 94.868117 \nL 469.389884 95.929546 \nL 469.675238 95.867336 \nL 470.254552 99.17575 \nL 470.591756 99.860542 \nL 470.833865 100.836307 \nL 471.119219 100.452749 \nL 471.404572 101.229695 \nL 471.689891 102.916814 \nL 471.983886 103.585187 \nL 472.260598 103.634985 \nL 472.5632 104.602875 \nL 472.83127 106.282461 \nL 473.125265 105.86196 \nL 473.427902 106.374633 \nL 474.015858 107.821349 \nL 474.292569 108.286995 \nL 474.577888 109.387578 \nL 474.906485 109.87332 \nL 475.157237 109.86767 \nL 475.727909 113.030692 \nL 476.307222 114.664196 \nL 476.592576 114.896317 \nL 477.171889 116.810361 \nL 477.457243 116.100826 \nL 477.751238 116.43504 \nL 478.036592 116.361278 \nL 478.32191 117.762039 \nL 478.615906 120.005885 \nL 479.186577 122.043619 \nL 479.480573 121.289824 \nL 479.765926 121.468149 \nL 480.051245 122.051104 \nL 480.327957 122.182756 \nL 480.915912 124.497141 \nL 481.209907 124.516772 \nL 481.495261 124.193537 \nL 481.780579 126.301927 \nL 482.074574 126.492452 \nL 482.359928 127.179248 \nL 482.636605 127.108961 \nL 482.939242 127.901842 \nL 483.224595 129.386943 \nL 483.509949 129.541368 \nL 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378.411131 \nL 540.569486 380.855045 \nL 540.872087 380.796148 \nL 541.157441 381.56645 \nL 541.442795 381.381671 \nL 541.73679 381.528751 \nL 542.022108 381.476306 \nL 542.307462 382.991504 \nL 542.59278 382.918881 \nL 542.886775 383.546692 \nL 543.172129 383.815554 \nL 543.466124 383.525941 \nL 544.330792 385.410332 \nL 544.607468 385.334212 \nL 544.901464 385.510489 \nL 545.480777 387.158243 \nL 545.766131 386.89403 \nL 546.345444 388.91089 \nL 546.630798 388.704694 \nL 546.924793 387.699338 \nL 547.20147 387.364467 \nL 547.789461 387.889969 \nL 548.360132 386.512429 \nL 548.654128 386.397239 \nL 548.939481 385.874155 \nL 549.804149 387.171865 \nL 550.383462 388.839749 \nL 550.668816 388.592336 \nL 550.954134 389.022209 \nL 551.524841 387.447849 \nL 551.81016 388.106306 \nL 552.39815 388.358675 \nL 552.683504 388.087805 \nL 552.977464 389.111043 \nL 553.262818 389.357787 \nL 553.833489 388.07418 \nL 554.127485 388.419413 \nL 554.412838 387.854293 \nL 554.706798 387.904818 \nL 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387.334112 \nL 570.270879 387.277949 \nL 570.556197 387.945271 \nL 571.135546 388.634008 \nL 571.420865 388.45991 \nL 571.71486 388.549446 \nL 572.000214 388.813785 \nL 572.285532 388.180113 \nL 572.570886 388.180288 \nL 572.864881 388.461263 \nL 573.435553 390.8902 \nL 573.729548 391.361556 \nL 574.014866 392.752001 \nL 574.594215 392.170988 \nL 575.164887 393.028933 \nL 575.744236 390.764303 \nL 576.029554 390.369612 \nL 576.32355 389.680767 \nL 576.608903 389.61664 \nL 576.894222 390.722688 \nL 577.188217 390.439019 \nL 577.473571 391.326614 \nL 577.776208 392.663966 \nL 578.052884 392.799019 \nL 578.329596 391.800198 \nL 578.623556 391.624373 \nL 578.917551 390.416556 \nL 579.202905 390.509174 \nL 579.488223 390.234857 \nL 579.782219 390.194831 \nL 580.067572 390.651236 \nL 580.352926 390.284362 \nL 580.646886 390.635635 \nL 580.923598 391.484375 \nL 581.260802 391.89505 \nL 581.546155 391.997167 \nL 581.796907 391.708319 \nL 582.08226 391.748185 \nL 582.37622 390.769387 \nL 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(2, figsize=(12,8))\n",
    "plt.title('Estimated (Kalman) Pos Y vs True Pos Y', fontweight='bold')\n",
    "plt.xlabel('Time') \n",
    "plt.ylabel('Y Position Estimate')   \n",
    "plt.plot(time,kal_y,'-', linewidth=3,label='Estimated y Value')\n",
    "\n",
    "plt.plot(time,true_y, linewidth=3, label='True y Value')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x118b7e2d0>"
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(3, figsize=(12,8))\n",
    "plt.title('Estimated (Kalman) Theta vs True Theta', fontweight='bold')\n",
    "plt.xlabel('Time') \n",
    "plt.ylabel('Theta Estimate')   \n",
    "plt.plot(time,kal_theta,'-', linewidth=3,label='Estimated Theta Value')\n",
    "plt.plot(time,true_theta, linewidth=3,label='True Theta Value')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x118bd8750>"
     },
     "metadata": {},
     "execution_count": 18
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
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428.584689 \nL 140.013985 428.590742 \nL 141.356393 429.651442 \nL 141.591528 430.572898 \nL 141.784323 429.970763 \nL 144.935814 430.30973 \nL 146.059398 428.687322 \nL 147.679775 428.46586 \nL 150.762175 426.693337 \nL 151.11573 425.60044 \nL 151.711424 426.042689 \nL 151.222292 425.909145 \nL 152.215736 427.234118 \nL 152.590063 426.807574 \nL 153.739768 426.49982 \nL 155.117354 424.834782 \nL 156.282085 425.270898 \nL 155.657573 425.891882 \nL 156.42743 426.004159 \nL 157.819128 425.918921 \nL 158.791066 425.460812 \nL 159.274513 425.554643 \nL 159.855932 424.341129 \nL 159.941426 424.749995 \nL 161.08513 424.678273 \nL 164.442169 425.70251 \nL 164.247795 426.042968 \nL 165.109523 426.367635 \nL 166.251806 428.100859 \nL 167.402273 428.886981 \nL 168.389509 429.278471 \nL 169.162046 428.975044 \nL 169.443919 429.478366 \nL 170.59526 429.0893 \nL 171.350877 428.99739 \nL 171.701477 427.784283 \nL 171.723505 428.174136 \nL 172.986926 427.994662 \nL 173.571205 428.380514 \nL 175.099295 429.091141 \nL 175.389972 428.22215 \nL 175.308114 428.488555 \nL 176.021976 429.290711 \nL 177.076863 429.554424 \nL 177.361801 430.00091 \nL 177.644236 429.654038 \nL 177.675586 429.687503 \nL 178.254482 429.451903 \nL 179.786637 430.44443 \nL 181.455964 430.969309 \nL 182.753149 430.566749 \nL 184.592946 430.404427 \nL 185.127776 431.070358 \nL 187.331711 428.394048 \nL 190.226037 428.180785 \nL 190.993833 429.05009 \nL 192.14927 427.652886 \nL 194.638649 427.36674 \nL 195.406697 426.417424 \nL 196.123661 426.819852 \nL 198.411161 426.320848 \nL 199.480337 426.382748 \nL 201.162929 425.565853 \nL 202.034368 424.79155 \nL 203.393637 424.925022 \nL 204.104517 425.722302 \nL 205.767744 426.235969 \nL 207.401746 425.941656 \nL 209.22395 425.780425 \nL 210.240641 426.434503 \nL 211.972622 426.349627 \nL 212.611285 426.514673 \nL 212.892304 425.828648 \nL 213.604949 425.858758 \nL 215.033429 426.382169 \nL 216.580748 426.264368 \nL 218.33285 425.619794 \nL 218.771014 426.089737 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437.430852 \nL 272.543105 436.423199 \nL 272.932238 435.538089 \nL 274.347049 434.543091 \nL 274.221126 435.260962 \nL 275.971328 434.607906 \nL 277.91882 433.569213 \nL 278.253695 434.017552 \nL 279.564449 433.956169 \nL 281.289109 433.205838 \nL 282.563211 433.617021 \nL 283.835056 433.541863 \nL 284.687717 432.902895 \nL 287.114407 432.724335 \nL 287.720293 432.555644 \nL 289.134532 433.100607 \nL 290.86321 432.323377 \nL 292.853768 431.669319 \nL 294.373696 431.375497 \nL 296.603531 432.17707 \nL 298.524338 432.187238 \nL 300.663325 430.543351 \nL 301.611351 431.51921 \nL 302.899909 430.420246 \nL 304.242612 430.420122 \nL 305.372003 431.448109 \nL 306.214261 430.821035 \nL 306.322248 430.251829 \nL 307.064737 430.010228 \nL 309.082072 429.795494 \nL 310.527681 429.009698 \nL 312.203423 427.566999 \nL 313.253042 427.186595 \nL 315.391335 427.245302 \nL 316.911784 427.589182 \nL 318.203396 427.290575 \nL 319.643209 427.518193 \nL 320.684161 426.342123 \nL 320.925065 427.31144 \nL 322.633858 428.01716 \nL 322.758662 428.351041 \nL 323.90121 427.550077 \nL 324.313237 427.92084 \nL 325.935731 427.272487 \nL 326.923612 426.446831 \nL 329.263191 426.48456 \nL 330.458543 426.366074 \nL 331.833624 425.789409 \nL 332.979744 426.824842 \nL 334.049755 426.429822 \nL 335.981632 425.99961 \nL 338.209485 426.494268 \nL 338.784748 426.945803 \nL 340.44639 427.138186 \nL 342.208316 426.848073 \nL 344.34038 426.661611 \nL 344.496661 426.405215 \nL 345.731174 427.001026 \nL 346.812445 427.295427 \nL 347.981759 426.713388 \nL 349.702705 427.740827 \nL 350.854353 428.005081 \nL 351.50087 426.881333 \nL 352.493061 427.146969 \nL 354.109858 427.125654 \nL 356.75817 427.740632 \nL 357.806512 427.394554 \nL 359.363623 428.16452 \nL 362.371266 427.648587 \nL 363.25777 426.972173 \nL 364.799677 426.479464 \nL 365.34305 425.840175 \nL 366.811082 424.590685 \nL 367.835509 425.328874 \nL 370.808312 425.162022 \nL 371.707297 425.544127 \nL 373.990997 425.966664 \nL 376.174113 427.288364 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428.249888 \nL 437.206808 428.103424 \nL 438.313728 427.375117 \nL 439.294325 426.104319 \nL 440.085499 425.50239 \nL 441.446057 426.224136 \nL 442.908169 426.608139 \nL 443.324644 426.428345 \nL 444.364955 425.843342 \nL 445.918687 426.562245 \nL 446.5937 426.688344 \nL 447.355545 427.124737 \nL 447.597993 426.164828 \nL 448.405289 426.549424 \nL 449.73009 426.391591 \nL 450.592942 427.275147 \nL 452.437882 428.029381 \nL 453.885266 428.409565 \nL 455.710767 427.998317 \nL 457.02873 428.134703 \nL 458.877464 428.463236 \nL 460.379022 429.784048 \nL 462.41418 431.150488 \nL 465.083602 431.875095 \nL 466.814365 431.915963 \nL 467.283738 432.077044 \nL 471.373349 430.4633 \nL 473.152647 430.721481 \nL 475.300892 430.362096 \nL 477.493809 431.277326 \nL 476.542575 430.838333 \nL 478.600243 431.462635 \nL 480.624345 431.580742 \nL 482.37519 432.05001 \nL 484.782761 430.492214 \nL 485.920961 430.599549 \nL 486.315777 431.358533 \nL 487.060859 432.004183 \nL 487.91782 431.660202 \nL 489.78621 431.561292 \nL 492.054204 432.342703 \nL 493.814732 432.971827 \nL 495.414796 432.88455 \nL 495.823677 431.91593 \nL 496.612175 431.964474 \nL 498.321691 432.736687 \nL 498.806112 431.549328 \nL 501.152633 431.112436 \nL 502.635137 431.174899 \nL 504.019625 431.90636 \nL 505.260799 431.783512 \nL 507.929535 431.285754 \nL 508.287513 432.100242 \nL 508.186072 431.838424 \nL 509.133011 431.877494 \nL 510.718012 431.705542 \nL 511.651212 431.286375 \nL 512.874024 431.004397 \nL 514.590802 430.192845 \nL 515.049226 430.727753 \nL 517.672768 430.743969 \nL 518.934831 430.238555 \nL 521.473683 430.45728 \nL 522.008872 431.631406 \nL 522.171716 430.411736 \nL 524.002313 430.412827 \nL 525.431176 430.234129 \nL 527.735373 428.441015 \nL 528.896893 428.16468 \nL 531.912732 428.036282 \nL 533.064468 428.644497 \nL 533.7778 429.229249 \nL 535.742692 431.013668 \nL 537.16609 431.599478 \nL 537.888934 432.887955 \nL 538.959737 433.088452 \nL 540.156826 434.263186 \nL 540.707364 434.245057 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426.320695 \nL 596.660042 426.448213 \nL 597.781242 425.790272 \nL 598.30729 425.712719 \nL 599.571575 424.622961 \nL 600.300745 424.632108 \nL 602.559634 424.070665 \nL 604.064472 423.508157 \nL 604.475919 423.653676 \nL 606.144573 423.431792 \nL 605.483485 422.41913 \nL 607.44949 422.388933 \nL 609.727614 421.454943 \nL 611.747533 421.805266 \nL 612.010719 421.588599 \nL 614.41109 419.993928 \nL 615.690717 418.69316 \nL 616.651387 419.360672 \nL 617.974433 419.157179 \nL 620.976522 418.009777 \nL 622.56044 417.975558 \nL 625.562724 417.086294 \nL 627.655993 416.511088 \nL 628.607914 416.935716 \nL 629.789852 416.777798 \nL 631.877247 415.811128 \nL 632.50468 415.107637 \nL 632.854502 413.810145 \nL 633.14119 413.986316 \nL 636.669253 413.508347 \nL 637.642298 412.628232 \nL 638.691187 413.039897 \nL 639.058082 412.729802 \nL 640.677068 411.96506 \nL 641.396468 410.437349 \nL 641.521683 408.859318 \nL 643.047958 408.13718 \nL 643.604604 407.452299 \nL 644.74316 406.329759 \nL 646.128144 405.491655 \nL 647.247768 404.616802 \nL 647.217247 404.09778 \nL 648.639018 403.015775 \nL 649.79558 401.366949 \nL 650.648777 399.671939 \nL 651.095953 398.237741 \nL 651.919995 397.520394 \nL 653.257998 396.946837 \nL 653.621008 396.522388 \nL 655.127951 394.51278 \nL 656.045908 393.976648 \nL 656.636668 392.136302 \nL 656.355216 390.931918 \nL 657.501265 389.733916 \nL 658.47993 387.453079 \nL 658.620481 386.333098 \nL 659.180292 385.266826 \nL 659.222165 384.170434 \nL 659.769673 380.64093 \nL 661.060698 379.129151 \nL 662.243688 376.869006 \nL 663.025116 374.851349 \nL 664.645427 373.063026 \nL 665.082072 373.297497 \nL 665.32917 372.005418 \nL 665.811488 371.167308 \nL 667.481358 369.191911 \nL 667.858736 368.411005 \nL 669.105033 366.680635 \nL 670.069224 364.885584 \nL 669.893709 364.810654 \nL 671.003858 363.475847 \nL 671.095612 362.61014 \nL 670.603165 361.712678 \nL 670.8155 359.751656 \nL 672.155705 358.497233 \nL 672.668829 357.56011 \nL 674.374429 356.077384 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298.492841 \nL 672.185683 297.098465 \nL 671.915302 294.978107 \nL 671.172482 294.030398 \nL 670.976273 292.615291 \nL 670.149654 287.859168 \nL 669.185671 287.4461 \nL 668.503589 285.567533 \nL 668.809735 284.602479 \nL 667.109513 282.722875 \nL 667.40815 281.928806 \nL 666.798605 281.297917 \nL 667.714596 280.384587 \nL 667.754806 278.606992 \nL 668.017544 277.08199 \nL 667.071529 274.205658 \nL 667.370179 270.920682 \nL 666.530454 269.403763 \nL 666.844598 267.632255 \nL 665.277077 266.574862 \nL 664.468796 265.471649 \nL 664.332312 264.463806 \nL 663.647345 263.387577 \nL 663.089642 261.452537 \nL 663.163209 260.002645 \nL 662.46745 257.892588 \nL 661.787972 253.769074 \nL 662.335522 253.7058 \nL 662.794763 252.099338 \nL 662.70521 249.94558 \nL 661.838472 248.525994 \nL 661.149748 246.559397 \nL 661.51754 244.652559 \nL 660.043318 242.242868 \nL 660.196596 240.091541 \nL 659.699077 238.869662 \nL 660.403905 236.510971 \nL 660.19122 235.587344 \nL 660.820999 234.585986 \nL 658.649932 234.095073 \nL 658.165122 233.281487 \nL 657.355866 231.095466 \nL 656.440292 229.379416 \nL 655.676158 228.51807 \nL 656.196019 227.247583 \nL 655.221633 226.85631 \nL 654.923855 226.360053 \nL 655.479538 223.978308 \nL 653.535372 222.364895 \nL 652.384859 221.073544 \nL 650.796238 219.863684 \nL 649.620852 217.428371 \nL 648.5607 217.725681 \nL 647.886166 217.616875 \nL 646.996611 217.098366 \nL 647.390343 216.5936 \nL 647.116649 214.705041 \nL 647.005232 211.157374 \nL 646.261565 209.032331 \nL 646.946996 206.332936 \nL 645.906473 204.445989 \nL 645.461223 202.225852 \nL 645.371844 199.249117 \nL 645.659833 197.100401 \nL 644.602849 195.05499 \nL 644.330701 193.230612 \nL 644.505572 192.459549 \nL 643.452212 190.993201 \nL 642.977492 189.973079 \nL 642.268503 186.788461 \nL 642.742324 185.205951 \nL 643.280326 184.328743 \nL 643.655237 182.591569 \nL 644.280502 181.77239 \nL 644.419899 180.197213 \nL 643.923852 179.123523 \nL 643.582512 177.054492 \nL 645.120222 176.405699 \nL 645.267933 175.044399 \nL 644.799773 173.619729 \nL 645.051694 172.86628 \nL 646.36223 171.130393 \nL 646.592348 169.069357 \nL 646.164355 166.773707 \nL 645.172392 165.366228 \nL 645.190778 162.224844 \nL 644.843289 160.782374 \nL 644.337749 160.212304 \nL 644.469672 159.765762 \nL 644.598773 157.10773 \nL 645.098314 156.212423 \nL 646.628153 154.287244 \nL 646.205343 153.596922 \nL 645.992819 151.045497 \nL 646.930437 150.72666 \nL 647.332586 150.711757 \nL 646.637243 150.027798 \nL 647.463652 148.699424 \nL 646.948538 147.277488 \nL 646.041407 147.293673 \nL 645.491161 145.988901 \nL 645.504074 143.413044 \nL 644.601355 142.654205 \nL 644.42373 141.652296 \nL 644.755093 140.421201 \nL 643.684075 139.586254 \nL 643.981985 137.599021 \nL 643.563651 135.875591 \nL 643.736803 134.543821 \nL 644.157254 133.14892 \nL 644.889324 132.584684 \nL 644.22908 130.891284 \nL 643.85854 130.308922 \nL 644.940695 128.41002 \nL 645.323157 126.746795 \nL 643.644803 125.718949 \nL 644.141261 124.373864 \nL 644.257127 122.194682 \nL 643.208073 122.109951 \nL 642.480247 120.574238 \nL 640.918343 119.399942 \nL 641.240661 117.121178 \nL 640.874133 115.981624 \nL 640.992103 113.884007 \nL 640.503811 112.830543 \nL 640.655866 110.893118 \nL 640.057011 109.836613 \nL 639.849881 107.683735 \nL 639.572514 106.144625 \nL 640.655335 104.721931 \nL 639.576688 103.074593 \nL 638.957345 101.697068 \nL 638.725064 100.215761 \nL 638.81735 98.837838 \nL 637.477165 97.536024 \nL 636.326914 96.035971 \nL 635.841296 94.693249 \nL 635.344977 92.528842 \nL 634.256513 91.298548 \nL 634.232286 90.879668 \nL 633.700928 89.816074 \nL 633.280635 88.670056 \nL 632.802811 86.837818 \nL 632.422451 86.903477 \nL 632.566973 86.120279 \nL 632.159546 86.202447 \nL 631.719468 85.571714 \nL 630.852595 84.041991 \nL 631.094871 82.707478 \nL 630.243568 81.530408 \nL 629.758803 80.556625 \nL 629.537422 79.128025 \nL 629.496123 77.906738 \nL 627.83541 78.023333 \nL 626.305915 74.979438 \nL 626.498508 75.476476 \nL 625.429738 74.406726 \nL 624.151334 74.071982 \nL 623.833339 73.469436 \nL 622.914062 72.077445 \nL 621.639019 71.380665 \nL 621.092685 69.253402 \nL 619.825654 67.270896 \nL 619.691346 65.501439 \nL 618.514729 64.510832 \nL 618.122887 64.698249 \nL 616.419212 64.153933 \nL 614.88233 63.151891 \nL 613.414737 63.363862 \nL 611.849716 63.304825 \nL 611.110067 64.48374 \nL 608.79862 64.331176 \nL 607.074564 64.661882 \nL 606.007442 63.690741 \nL 605.382209 63.828737 \nL 603.63426 64.819304 \nL 602.81279 64.531757 \nL 601.274842 63.825266 \nL 598.094172 62.767222 \nL 596.779437 62.085589 \nL 594.95081 62.416611 \nL 593.778653 61.429658 \nL 591.107244 60.54358 \nL 589.963785 60.446116 \nL 589.570308 59.790683 \nL 587.948918 60.383163 \nL 586.576619 59.958935 \nL 584.841362 58.887687 \nL 581.076005 58.189758 \nL 580.391781 57.875968 \nL 579.329444 57.935776 \nL 578.027763 58.790742 \nL 576.12861 59.199285 \nL 573.278664 58.887871 \nL 571.823793 60.101363 \nL 570.813406 59.898853 \nL 569.756352 60.185989 \nL 569.006311 61.403907 \nL 567.374934 59.612301 \nL 567.208059 59.915115 \nL 566.192672 59.144879 \nL 565.622578 58.928784 \nL 563.482049 59.290593 \nL 559.884 58.381296 \nL 558.812658 59.063234 \nL 555.390352 58.759092 \nL 555.154883 59.12513 \nL 553.706714 59.013525 \nL 552.321129 57.855192 \nL 550.448109 58.1396 \nL 549.47079 58.038886 \nL 548.383789 58.940753 \nL 547.031331 58.555843 \nL 545.145088 57.816108 \nL 543.442573 59.086318 \nL 540.963446 59.800043 \nL 540.104245 60.34239 \nL 538.715449 60.385691 \nL 538.296637 59.831265 \nL 537.153863 59.481215 \nL 535.296516 59.498059 \nL 534.499699 58.999704 \nL 533.945933 58.345109 \nL 532.249465 57.456294 \nL 531.215709 56.656119 \nL 530.068833 56.660795 \nL 528.374427 56.249729 \nL 527.086375 56.377691 \nL 525.801383 54.87298 \nL 524.23395 54.353451 \nL 522.023233 54.192384 \nL 520.962255 55.106517 \nL 519.530977 55.729587 \nL 518.558627 55.38146 \nL 517.510086 56.419145 \nL 516.003582 55.574738 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465.184213 53.172406 \nL 463.889693 51.121079 \nL 462.487836 52.097511 \nL 460.635916 51.630564 \nL 459.419887 51.467574 \nL 458.090137 51.611074 \nL 457.343289 51.196377 \nL 455.986085 51.369653 \nL 455.092076 50.911341 \nL 453.7176 50.59307 \nL 452.720877 50.910338 \nL 452.316752 50.874484 \nL 450.054052 50.41535 \nL 448.265243 50.343938 \nL 446.791001 49.830485 \nL 445.759019 50.191416 \nL 444.366009 49.544916 \nL 441.993644 48.178305 \nL 439.865665 48.407337 \nL 438.310214 47.642581 \nL 437.370095 48.244841 \nL 435.781177 47.736143 \nL 434.105494 47.577724 \nL 431.569464 48.770586 \nL 429.820733 48.458245 \nL 427.600993 47.888031 \nL 425.254026 46.357674 \nL 423.719111 46.449141 \nL 421.765649 45.944974 \nL 421.431836 44.980265 \nL 419.84508 44.449344 \nL 418.926726 43.456473 \nL 416.400073 44.177495 \nL 415.323922 44.085691 \nL 412.971133 44.757129 \nL 411.505885 44.862304 \nL 410.563937 45.130535 \nL 409.016316 44.548508 \nL 406.655389 43.954086 \nL 404.92703 43.715369 \nL 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(4, figsize=(12,8))\n",
    "plt.title('Robot Position in 2-D Env : Kalman vs True', fontweight='bold')\n",
    "plt.plot(kal_x,kal_y,'-', linewidth=3,label='Estimated Path')\n",
    "plt.plot(true_x,true_y, linewidth=3, label='True Path')\n",
    "plt.legend(loc='best')"
   ]
  }
 ]
}