Landmark_Detection_and_Tracking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a function, `slam`, which takes in six parameters as input and returns the vector `mu`. \n",
    "> `mu` contains the (x,y) coordinate locations of the robot as it moves, and the positions of landmarks that it senses in the world\n",
    "\n",
    "The vector, `mu`, should have (x, y) coordinates interlaced, for example, if there were 2 poses and 2 landmarks, `mu` will look like the following, where `P` is the robot position and `L` the landmark position:\n",
    "```\n",
    "mu =  matrix([[Px0],\n",
    "              [Py0],\n",
    "              [Px1],\n",
    "              [Py1],\n",
    "              [Lx0],\n",
    "              [Ly0],\n",
    "              [Lx1],\n",
    "              [Ly1]])\n",
    "```\n",
    "\n",
    "You can see that `mu` holds the poses first `(x0, y0), (x1, y1), ...,` then the landmark locations at the end of the matrix; we consider a `nx1` matrix to be a vector.\n",
    "\n",
    "## Generating an environment\n",
    "\n",
    "In this example, we will make our own data using the `make_data` function, which generates a world grid with landmarks in it and then generates data by placing a robot in that world and moving and sensing over some numer of time steps. The data is collected as an instantiated robot moves and senses in a world. SLAM function will take in this data as input.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the world\n",
    "\n",
    "`data` holds the sensors measurements and motion of your robot over time. It stores the measurements as `data[i][0]` and the motion as `data[i][1]`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Landmarks:  [[80, 82], [69, 92], [89, 56], [8, 39], [60, 89]]\n",
      "Robot: [x=86.92632 y=35.21455]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from helpers import make_data\n",
    "\n",
    "# world parameters\n",
    "num_landmarks      = 5        # number of landmarks\n",
    "N                  = 20       # time steps\n",
    "world_size         = 100.0    # size of world (square)\n",
    "\n",
    "# robot parameters\n",
    "measurement_range  = 50.0     # range at which we can sense landmarks\n",
    "motion_noise       = 2.0      # noise in robot motion\n",
    "measurement_noise  = 2.0      # noise in the measurements\n",
    "distance           = 20.0     # distance by which robot (intends to) move each iteratation \n",
    "\n",
    "\n",
    "# make_data instantiates a robot, AND generates random landmarks for a given world size and number of landmarks\n",
    "data = make_data(N, num_landmarks, world_size, measurement_range, motion_noise, measurement_noise, distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A note on `make_data`\n",
    "\n",
    "The function above, `make_data`, takes in so many world and robot motion/sensor parameters because it is responsible for:\n",
    "1. Instantiating a robot (using the robot class)\n",
    "2. Creating a grid world with landmarks in it\n",
    "\n",
    "The `data` this returns is an array that holds information about **robot sensor measurements** and **robot motion** `(dx, dy)` that is collected over a number of time steps, `N`.\n",
    "\n",
    "\n",
    "In `data` the measurement and motion data can be accessed from the first and second index in the columns of the data array. See the following code for an example, where `i` is the time step:\n",
    "```\n",
    "measurement = data[i][0]\n",
    "motion = data[i][1]\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example measurements: \n",
      " [[0, -36.587230813056365, -38.81906830702575], [1, 43.757644252637476, 43.58950985347909], [2, 4.756708481050532, -1.188161965947427], [3, 9.53783409277398, 45.445924792792376], [4, -35.47689880220784, 2.373847386791178]]\n",
      "\n",
      "\n",
      "Example motion: \n",
      " [19.762150333489952, 3.075290912506169]\n"
     ]
    }
   ],
   "source": [
    "# print out some stats about the data\n",
    "time_step = 0\n",
    "\n",
    "print('Example measurements: \\n', data[time_step][0])\n",
    "print('\\n')\n",
    "print('Example motion: \\n', data[time_step][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try changing the value of `time_step`, you should see that the list of measurements varies based on what in the world the robot sees after it moves. As you know from the first notebook, the robot can only sense so far and with a certain amount of accuracy in the measure of distance between its location and the location of landmarks. The motion of the robot always is a vector with two values: one for x and one for y displacement. This structure will be useful to keep in mind as you traverse this data in your implementation of slam."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Constraints\n",
    "\n",
    "One of the most challenging tasks here will be to create and modify the constraint matrix and vector: omega and xi.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TODO: Write a function that initializes omega and xi\n",
    "\n",
    "Complete the function `initialize_constraints` so that it returns `omega` and `xi` constraints for the starting position of the robot. Any values that we do not yet know should be initialized with the value `0`. Assume that our robot starts out in exactly the middle of the world with 100% confidence (no motion or measurement noise at this point). The inputs `N` time steps, `num_landmarks`, and `world_size` should give all the information you need to construct intial constraints of the correct size and starting values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_constraints(N, num_landmarks, world_size):\n",
    "    ''' This function takes in a number of time steps N, number of landmarks, and a world_size,\n",
    "        and returns initialized constraint matrices, omega and xi.'''\n",
    "    \n",
    "    ## Define and store the size (rows/cols) of the constraint matrix in a variable\n",
    "    dim = 2*N+2*num_landmarks\n",
    "    omega_shape = (dim ,dim )\n",
    "    xi_shape = (dim, 1)\n",
    "    ## Define the constraint matrix, Omega, with two initial \"strength\" values\n",
    "    ## for the initial x, y location of our robot\n",
    "    omega = np.zeros(omega_shape)\n",
    "    \n",
    "    omega[0,0] = 1.0\n",
    "    omega[1,1] = 1.0\n",
    "    \n",
    "    ## Define the constraint *vector*, xi\n",
    "    ## you can assume that the robot starts out in the middle of the world with 100% confidence\n",
    "    xi = np.zeros(xi_shape)\n",
    "    \n",
    "    xi[0,0] = world_size/2\n",
    "    xi[1,0] = world_size/2\n",
    "    \n",
    "    return omega, xi   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test \n",
    "\n",
    "Since `slam` relies on creating and updating constraint matrices, `omega` and `xi` to account for robot sensor measurements and motion, let's check that they initialize as expected for any given parameters.\n",
    "\n",
    "Below, you'll find some test code that allows you to visualize the results of function `initialize_constraints`. We are using the [seaborn](https://seaborn.pydata.org/) library for visualization.\n",
    "\n",
    "**Please change the test values of N, landmarks, and world_size and see the results**. Be careful not to use these values as input into your final smal function.\n",
    "\n",
    "This code assumes that you have created one of each constraint: `omega` and `xi`, but you can change and add to this code, accordingly. The constraints should vary in size with the number of time steps and landmarks as these values affect the number of poses a robot will take `(Px0,Py0,...Pxn,Pyn)` and landmark locations `(Lx0,Ly0,...Lxn,Lyn)` whose relationships should be tracked in the constraint matrices. Recall that `omega` holds the weights of each variable and `xi` holds the value of the sum of these variables. You'll need the `world_size` to determine the starting pose of the robot in the world and fill in the initial values for `xi`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data viz resources\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a small N and world_size (small for ease of visualization)\n",
    "N_test = 5\n",
    "num_landmarks_test = 2\n",
    "small_world = 10\n",
    "\n",
    "# initialize the constraints\n",
    "initial_omega, initial_xi = initialize_constraints(N_test, num_landmarks_test, small_world)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ebbe489e8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6edcb08a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define figure size\n",
    "plt.rcParams[\"figure.figsize\"] = (10,7)\n",
    "\n",
    "# display omega\n",
    "sns.heatmap(DataFrame(initial_omega), cmap='Blues', annot=True, linewidths=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6eb9af9358>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eb9ad8080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define  figure size\n",
    "plt.rcParams[\"figure.figsize\"] = (1,7)\n",
    "\n",
    "# display xi\n",
    "sns.heatmap(DataFrame(initial_xi), cmap='Oranges', annot=True, linewidths=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## SLAM inputs \n",
    "\n",
    "In addition to `data`, your slam function will also take in:\n",
    "* N -   The number of time steps that a robot will be moving and sensing\n",
    "* num_landmarks - The number of landmarks in the world\n",
    "* world_size - The size (w/h) of your world\n",
    "* motion_noise - The noise associated with motion; the update confidence for motion should be `1.0/motion_noise`\n",
    "* measurement_noise - The noise associated with measurement/sensing; the update weight for measurement should be `1.0/measurement_noise`\n",
    "\n",
    "#### A note on noise\n",
    "\n",
    "Recall that `omega` holds the relative \"strengths\" or weights for each position variable, and you can update these weights by accessing the correct index in omega `omega[row][col]` and *adding/subtracting* `1.0/noise` where `noise` is measurement or motion noise. `Xi` holds actual position values, and so to update `xi` you'll do a similar addition process only using the actual value of a motion or measurement. So for a vector index `xi[row][0]` you will end up adding/subtracting one measurement or motion divided by their respective `noise`.\n",
    "\n",
    "\n",
    "#### Updating with motion and measurements\n",
    "\n",
    "With a 2D omega and xi structure as shown above (in earlier cells), you'll have to be mindful about how you update the values in these constraint matrices to account for motion and measurement constraints in the x and y directions. Recall that the solution to these matrices (which holds all values for robot poses `P` and landmark locations `L`) is the vector, `mu`, which can be computed at the end of the construction of omega and xi as the inverse of omega times xi: $\\mu = \\Omega^{-1}\\xi$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.linalg import inv\n",
    "from numpy.linalg import det\n",
    "## slam takes in 6 arguments and returns mu, \n",
    "## mu is the entire path traversed by a robot (all x,y poses) *and* all landmarks locations\n",
    "def slam(data, N, num_landmarks, world_size, motion_noise, measurement_noise):\n",
    "    \n",
    "    ## Use your initilization to create constraint matrices, omega and xi    \n",
    "    omega, xi = initialize_constraints(N, num_landmarks, world_size)\n",
    "    \n",
    "    ## Iterate through each time step in the data\n",
    "    ## get all the motion and measurement data as you iterate\n",
    "           \n",
    "    ## update the constraint matrix/vector to account for all *measurements*\n",
    "    ## this should be a series of additions that take into account the measurement noise                       \n",
    "    \n",
    "    ## update the constraint matrix/vector to account for all *motion* and motion noise\n",
    "    \n",
    "    for k, x in enumerate(data):\n",
    "        # robot pose index in omega\n",
    "        i_robot = k * 2    \n",
    "        measurment = x[0]\n",
    "        motion = x[1]\n",
    "        # update based on measurment  \n",
    "        for i in range(len(measurment)):\n",
    "            # landmark index in omega\n",
    "            i_landmark = 2 * (N + measurment[i][0])     \n",
    "            for j in range(2):\n",
    "                omega[i_landmark+j][i_landmark+j] += 1.0/measurement_noise\n",
    "                omega[i_robot+j][i_robot+j]       += 1.0/measurement_noise\n",
    "                omega[i_robot+j][i_landmark+j]    -= 1.0/measurement_noise     \n",
    "                omega[i_landmark+j][i_robot+j]    -= 1.0/measurement_noise\n",
    "                xi[i_landmark+j][0]               += measurment[i][1+j]/measurement_noise\n",
    "                xi[i_robot+j][0]                  -= measurment[i][1+j]/measurement_noise\n",
    "            \n",
    "        # update based on robot motion\n",
    "        for j in range(4):    \n",
    "            omega[i_robot+j][i_robot+j] +=   1.0/motion_noise\n",
    "        for j in range(2):\n",
    "            omega[i_robot+j][i_robot+j+2] -= 1.0/motion_noise\n",
    "            omega[i_robot+j+2][i_robot+j] -= 1.0/motion_noise\n",
    "            xi[i_robot+j][0]              -= motion[j]/motion_noise\n",
    "            xi[i_robot+j+2][0]            += motion[j]/motion_noise\n",
    "            \n",
    "    ##  After iterating through all the data\n",
    "    ## Compute the best estimate of poses and landmark positions\n",
    "    ## using the formula, omega_inverse * Xi\n",
    "    \n",
    "    mu = np.dot(inv(omega),xi)\n",
    "    return mu, omega, xi # return `mu`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6eb9a4c828>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eb984e390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu,omega,xi = slam(data, N, num_landmarks, world_size, motion_noise, measurement_noise)\n",
    "# figure size\n",
    "plt.rcParams[\"figure.figsize\"] = (20,18)\n",
    "# display heatmap of omega\n",
    "sns.heatmap(DataFrame(omega), cmap='Blues', annot=True, linewidths=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6eb997ca58>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eb82b9e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure size\n",
    "plt.rcParams[\"figure.figsize\"] = (1,18)\n",
    "# display heatmap of xi\n",
    "sns.heatmap(DataFrame(xi), cmap='Oranges', annot=True, linewidths=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a helper function that creates a list of poses and of landmarks for ease of printing\n",
    "# this only works for the suggested constraint architecture of interlaced x,y poses\n",
    "def get_poses_landmarks(mu, N):\n",
    "    # create a list of poses\n",
    "    poses = []\n",
    "    for i in range(N):\n",
    "        poses.append((mu[2*i].item(), mu[2*i+1].item()))\n",
    "\n",
    "    # create a list of landmarks\n",
    "    landmarks = []\n",
    "    for i in range(num_landmarks):\n",
    "        landmarks.append((mu[2*(N+i)].item(), mu[2*(N+i)+1].item()))\n",
    "\n",
    "    # return completed lists\n",
    "    return poses, landmarks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_all(poses, landmarks):\n",
    "    print('\\n')\n",
    "    print('Estimated Poses:')\n",
    "    for i in range(len(poses)):\n",
    "        print('['+', '.join('%.3f'%p for p in poses[i])+']')\n",
    "    print('\\n')\n",
    "    print('Estimated Landmarks:')\n",
    "    for i in range(len(landmarks)):\n",
    "        print('['+', '.join('%.3f'%l for l in landmarks[i])+']')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run SLAM\n",
    "\n",
    "### What to Expect\n",
    "\n",
    "The `data` that is generated is random, but the number, `N`, or time steps that the robot was expected to move and the `num_landmarks` in the world (which your implementation of `slam` should see and estimate a position for was specified. Robot should also start with an estimated pose in the very center of your square world, whose size is defined by `world_size`.\n",
    "\n",
    "With these values in mind, you should expect to see a result that displays two lists:\n",
    "1. **Estimated poses**, a list of (x, y) pairs that is exactly `N` in length since this is how many motions your robot has taken. The very first pose should be the center of your world, i.e. `[50.000, 50.000]` for a world that is 100.0 in square size.\n",
    "2. **Estimated landmarks**, a list of landmark positions (x, y) that is exactly `num_landmarks` in length. \n",
    "\n",
    "#### Landmark Locations\n",
    "\n",
    "If you refer back to the printout of *exact* landmark locations when this data was created, you should see values that are very similar to those coordinates, but not quite (since `slam` must account for noise in motion and measurement)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Estimated Poses:\n",
      "[50.000, 50.000]\n",
      "[69.720, 51.251]\n",
      "[88.469, 53.500]\n",
      "[94.795, 33.355]\n",
      "[99.948, 14.250]\n",
      "[85.740, 28.870]\n",
      "[71.306, 42.103]\n",
      "[57.100, 56.433]\n",
      "[41.823, 71.263]\n",
      "[26.821, 86.253]\n",
      "[13.740, 98.965]\n",
      "[32.965, 89.493]\n",
      "[50.762, 82.620]\n",
      "[69.798, 74.701]\n",
      "[87.281, 66.090]\n",
      "[96.473, 49.227]\n",
      "[80.702, 60.580]\n",
      "[62.927, 70.105]\n",
      "[44.864, 81.618]\n",
      "[28.125, 92.562]\n",
      "\n",
      "\n",
      "Estimated Landmarks:\n",
      "[13.250, 11.190]\n",
      "[92.241, 94.365]\n",
      "[55.535, 49.868]\n",
      "[60.289, 95.310]\n",
      "[14.714, 52.493]\n"
     ]
    }
   ],
   "source": [
    "mu,_,_ = slam(data, N, num_landmarks, world_size, motion_noise, measurement_noise)\n",
    "\n",
    "# print out the resulting landmarks and poses\n",
    "if(mu is not None):\n",
    "    # get the lists of poses and landmarks\n",
    "    # and print them out\n",
    "    poses, landmarks = get_poses_landmarks(mu, N)\n",
    "    print_all(poses, landmarks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the constructed world\n",
    "\n",
    "Finally, using the `display_world` code from the `helpers.py` file (which was also used in the first notebook), we can actually visualize what has been coded with `slam`: the final position of the robot and the positon of landmarks, created from only motion and measurement data!\n",
    "\n",
    "**Note that these should be very similar to the printed *true* landmark locations and final pose from our call to `make_data` early in this notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last pose:  (28.12462613453829, 92.56222308734567)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eb9428c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the helper function\n",
    "from helpers import display_world\n",
    "\n",
    "# Display the final world!\n",
    "\n",
    "# define figure size\n",
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "\n",
    "# check if poses has been created\n",
    "if 'poses' in locals():\n",
    "    # print out the last pose\n",
    "    print('Last pose: ', poses[-1])\n",
    "    # display the last position of the robot *and* the landmark positions\n",
    "    display_world(int(world_size), poses[-1], landmarks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "True last pose : ```[x=28.22888 y=92.08595]``` and estimated last pose : ```[28.12462613453829, 92.56222308734567]``` The distance between the two points: D=√((x2−x1)2+(y2−y1)2) = √((28.22888−28.12462)2+(92.08595-92.56222)2) ≈ 0.48\n",
    "\n",
    "\n",
    "\n",
    "Due to the measurment and movement noise, the poses are different (Pose estimates have a level of uncertainty associated with them).With less noise in measurements i.e better sensors, we can reduce this difference in the estimated and true pose."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Estimated Poses:\n",
      "[50.000, 50.000]\n",
      "[37.973, 33.652]\n",
      "[26.185, 18.155]\n",
      "[13.745, 2.116]\n",
      "[28.097, 16.783]\n",
      "[42.384, 30.902]\n",
      "[55.831, 44.497]\n",
      "[70.857, 59.699]\n",
      "[85.697, 75.543]\n",
      "[74.011, 92.434]\n",
      "[53.544, 96.454]\n",
      "[34.525, 100.080]\n",
      "[48.623, 83.953]\n",
      "[60.197, 68.107]\n",
      "[73.778, 52.935]\n",
      "[87.132, 38.538]\n",
      "[80.303, 20.508]\n",
      "[72.798, 2.945]\n",
      "[55.245, 13.255]\n",
      "[37.416, 22.317]\n",
      "\n",
      "\n",
      "Estimated Landmarks:\n",
      "[82.956, 13.539]\n",
      "[70.495, 74.141]\n",
      "[36.740, 61.281]\n",
      "[18.698, 66.060]\n",
      "[20.635, 16.875]\n"
     ]
    }
   ],
   "source": [
    "# Here is the data and estimated outputs for test case 1\n",
    "\n",
    "test_data1 = [[[[1, 19.457599255548065, 23.8387362100849], [2, -13.195807561967236, 11.708840328458608], [3, -30.0954905279171, 15.387879242505843]], [-12.2607279422326, -15.801093326936487]], [[[2, -0.4659930049620491, 28.088559771215664], [4, -17.866382374890936, -16.384904503932]], [-12.2607279422326, -15.801093326936487]], [[[4, -6.202512900833806, -1.823403210274639]], [-12.2607279422326, -15.801093326936487]], [[[4, 7.412136480918645, 15.388585962142429]], [14.008259661173426, 14.274756084260822]], [[[4, -7.526138813444998, -0.4563942429717849]], [14.008259661173426, 14.274756084260822]], [[[2, -6.299793150150058, 29.047830407717623], [4, -21.93551130411791, -13.21956810989039]], [14.008259661173426, 14.274756084260822]], [[[1, 15.796300959032276, 30.65769689694247], [2, -18.64370821983482, 17.380022987031367]], [14.008259661173426, 14.274756084260822]], [[[1, 0.40311325410337906, 14.169429532679855], [2, -35.069349468466235, 2.4945558982439957]], [14.008259661173426, 14.274756084260822]], [[[1, -16.71340983241936, -2.777000269543834]], [-11.006096015782283, 16.699276945166858]], [[[1, -3.611096830835776, -17.954019226763958]], [-19.693482634035977, 3.488085684573048]], [[[1, 18.398273354362416, -22.705102332550947]], [-19.693482634035977, 3.488085684573048]], [[[2, 2.789312482883833, -39.73720193121324]], [12.849049222879723, -15.326510824972983]], [[[1, 21.26897046581808, -10.121029799040915], [2, -11.917698965880655, -23.17711662602097], [3, -31.81167947898398, -16.7985673023331]], [12.849049222879723, -15.326510824972983]], [[[1, 10.48157743234859, 5.692957082575485], [2, -22.31488473554935, -5.389184118551409], [3, -40.81803984305378, -2.4703329790238118]], [12.849049222879723, -15.326510824972983]], [[[0, 10.591050242096598, -39.2051798967113], [1, -3.5675572049297553, 22.849456408289125], [2, -38.39251065320351, 7.288990306029511]], [12.849049222879723, -15.326510824972983]], [[[0, -3.6225556479370766, -25.58006865235512]], [-7.8874682868419965, -18.379005523261092]], [[[0, 1.9784503557879374, -6.5025974151499]], [-7.8874682868419965, -18.379005523261092]], [[[0, 10.050665232782423, 11.026385307998742]], [-17.82919359778298, 9.062000642947142]], [[[0, 26.526838150174818, -0.22563393232425621], [4, -33.70303936886652, 2.880339841013677]], [-17.82919359778298, 9.062000642947142]]]\n",
    "\n",
    "##  Test Case 1\n",
    "##\n",
    "# Estimated Pose(s):\n",
    "#     [50.000, 50.000]\n",
    "#     [37.858, 33.921]\n",
    "#     [25.905, 18.268]\n",
    "#     [13.524, 2.224]\n",
    "#     [27.912, 16.886]\n",
    "#     [42.250, 30.994]\n",
    "#     [55.992, 44.886]\n",
    "#     [70.749, 59.867]\n",
    "#     [85.371, 75.230]\n",
    "#     [73.831, 92.354]\n",
    "#     [53.406, 96.465]\n",
    "#     [34.370, 100.134]\n",
    "#     [48.346, 83.952]\n",
    "#     [60.494, 68.338]\n",
    "#     [73.648, 53.082]\n",
    "#     [86.733, 38.197]\n",
    "#     [79.983, 20.324]\n",
    "#     [72.515, 2.837]\n",
    "#     [54.993, 13.221]\n",
    "#     [37.164, 22.283]\n",
    "\n",
    "\n",
    "# Estimated Landmarks:\n",
    "#     [82.679, 13.435]\n",
    "#     [70.417, 74.203]\n",
    "#     [36.688, 61.431]\n",
    "#     [18.705, 66.136]\n",
    "#     [20.437, 16.983]\n",
    "\n",
    "\n",
    "mu_1, _, _ = slam(test_data1, 20, 5, 100.0, 2.0, 2.0)\n",
    "poses, landmarks = get_poses_landmarks(mu_1, 20)\n",
    "print_all(poses, landmarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Estimated Poses:\n",
      "[50.000, 50.000]\n",
      "[69.181, 45.665]\n",
      "[87.743, 39.703]\n",
      "[76.270, 56.311]\n",
      "[64.317, 72.176]\n",
      "[52.257, 88.154]\n",
      "[44.059, 69.401]\n",
      "[37.002, 49.918]\n",
      "[30.924, 30.955]\n",
      "[23.508, 11.419]\n",
      "[34.180, 27.133]\n",
      "[44.155, 43.846]\n",
      "[54.806, 60.920]\n",
      "[65.698, 78.546]\n",
      "[77.468, 95.626]\n",
      "[96.802, 98.821]\n",
      "[75.957, 99.971]\n",
      "[70.200, 81.181]\n",
      "[64.054, 61.723]\n",
      "[58.107, 42.628]\n",
      "\n",
      "\n",
      "Estimated Landmarks:\n",
      "[76.779, 42.887]\n",
      "[85.065, 77.438]\n",
      "[13.548, 95.652]\n",
      "[59.449, 39.595]\n",
      "[69.263, 94.240]\n"
     ]
    }
   ],
   "source": [
    "# Here is the data and estimated outputs for test case 2\n",
    "\n",
    "test_data2 = [[[[0, 26.543274387283322, -6.262538160312672], [3, 9.937396825799755, -9.128540360867689]], [18.92765331253674, -6.460955043986683]], [[[0, 7.706544739722961, -3.758467215445748], [1, 17.03954411948937, 31.705489938553438], [3, -11.61731288777497, -6.64964096716416]], [18.92765331253674, -6.460955043986683]], [[[0, -12.35130507136378, 2.585119104239249], [1, -2.563534536165313, 38.22159657838369], [3, -26.961236804740935, -0.4802312626141525]], [-11.167066095509824, 16.592065417497455]], [[[0, 1.4138633151721272, -13.912454837810632], [1, 8.087721200818589, 20.51845934354381], [3, -17.091723454402302, -16.521500551709707], [4, -7.414211721400232, 38.09191602674439]], [-11.167066095509824, 16.592065417497455]], [[[0, 12.886743222179561, -28.703968411636318], [1, 21.660953298391387, 3.4912891084614914], [3, -6.401401414569506, -32.321583037341625], [4, 5.034079343639034, 23.102207946092893]], [-11.167066095509824, 16.592065417497455]], [[[1, 31.126317672358578, -10.036784369535214], [2, -38.70878528420893, 7.4987265861424595], [4, 17.977218575473767, 6.150889254289742]], [-6.595520680493778, -18.88118393939265]], [[[1, 41.82460922922086, 7.847527392202475], [3, 15.711709540417502, -30.34633659912818]], [-6.595520680493778, -18.88118393939265]], [[[0, 40.18454208294434, -6.710999804403755], [3, 23.019508919299156, -10.12110867290604]], [-6.595520680493778, -18.88118393939265]], [[[3, 27.18579315312821, 8.067219022708391]], [-6.595520680493778, -18.88118393939265]], [[], [11.492663265706092, 16.36822198838621]], [[[3, 24.57154567653098, 13.461499960708197]], [11.492663265706092, 16.36822198838621]], [[[0, 31.61945290413707, 0.4272295085799329], [3, 16.97392299158991, -5.274596836133088]], [11.492663265706092, 16.36822198838621]], [[[0, 22.407381798735177, -18.03500068379259], [1, 29.642444125196995, 17.3794951934614], [3, 4.7969752441371645, -21.07505361639969], [4, 14.726069092569372, 32.75999422300078]], [11.492663265706092, 16.36822198838621]], [[[0, 10.705527984670137, -34.589764174299596], [1, 18.58772336795603, -0.20109708164787765], [3, -4.839806195049413, -39.92208742305105], [4, 4.18824810165454, 14.146847823548889]], [11.492663265706092, 16.36822198838621]], [[[1, 5.878492140223764, -19.955352450942357], [4, -7.059505455306587, -0.9740849280550585]], [19.628527845173146, 3.83678180657467]], [[[1, -11.150789592446378, -22.736641053247872], [4, -28.832815721158255, -3.9462962046291388]], [-19.841703647091965, 2.5113335861604362]], [[[1, 8.64427397916182, -20.286336970889053], [4, -5.036917727942285, -6.311739993868336]], [-5.946642674882207, -19.09548221169787]], [[[0, 7.151866679283043, -39.56103232616369], [1, 16.01535401373368, -3.780995345194027], [4, -3.04801331832137, 13.697362774960865]], [-5.946642674882207, -19.09548221169787]], [[[0, 12.872879480504395, -19.707592098123207], [1, 22.236710716903136, 16.331770792606406], [3, -4.841206109583004, -21.24604435851242], [4, 4.27111163223552, 32.25309748614184]], [-5.946642674882207, -19.09548221169787]]] \n",
    "\n",
    "\n",
    "##  Test Case 2\n",
    "##\n",
    "# Estimated Pose(s):\n",
    "#     [50.000, 50.000]\n",
    "#     [69.035, 45.061]\n",
    "#     [87.655, 38.971]\n",
    "#     [76.084, 55.541]\n",
    "#     [64.283, 71.684]\n",
    "#     [52.396, 87.887]\n",
    "#     [44.674, 68.948]\n",
    "#     [37.532, 49.680]\n",
    "#     [31.392, 30.893]\n",
    "#     [24.796, 12.012]\n",
    "#     [33.641, 26.440]\n",
    "#     [43.858, 43.560]\n",
    "#     [54.735, 60.659]\n",
    "#     [65.884, 77.791]\n",
    "#     [77.413, 94.554]\n",
    "#     [96.740, 98.020]\n",
    "#     [76.149, 99.586]\n",
    "#     [70.211, 80.580]\n",
    "#     [64.130, 61.270]\n",
    "#     [58.183, 42.175]\n",
    "\n",
    "\n",
    "# Estimated Landmarks:\n",
    "#     [76.777, 42.415]\n",
    "#     [85.109, 76.850]\n",
    "#     [13.687, 95.386]\n",
    "#     [59.488, 39.149]\n",
    "#     [69.283, 93.654]\n",
    "\n",
    "\n",
    "mu_2, _, _ = slam(test_data2, 20, 5, 100.0, 2.0, 2.0)\n",
    "poses, landmarks = get_poses_landmarks(mu_2, 20)\n",
    "print_all(poses, landmarks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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