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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n",
    "Atlanta, Georgia 30332-0415\n",
    "All Rights Reserved\n",
    "\n",
    "Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n",
    "\n",
    "See LICENSE for the license information\n",
    "\n",
    "A 2D Range SLAM example, with iSAM and smart range factors\n",
    "\n",
    "Author: Frank Dellaert"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is second UWB ranging dataset, B2 or \"plaza 2\", from\n",
    "\n",
    "> \"Navigating with Ranging Radios: Five Data Sets with Ground Truth\", by Joseph Djugash, Bradley Hamner, and Stephan Roth, available at https://www.ri.cmu.edu/pub_files/2009/9/Final_5datasetsRangingRadios.pdf\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pylint: disable=invalid-name, E1101\n",
    "\n",
    "from gtsam import Point2, Pose2\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import gtsam\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from gtsam.utils import plot\n",
    "from numpy.random import default_rng\n",
    "\n",
    "rng = default_rng()\n",
    "\n",
    "NM = gtsam.noiseModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 4090 odometry entries.\n"
     ]
    }
   ],
   "source": [
    "# load the odometry\n",
    "# DR: Odometry Input (delta distance traveled and delta heading change)\n",
    "#    Time (sec)  Delta Distance Traveled (m) Delta Heading (rad)\n",
    "odometry = {}\n",
    "data_file = gtsam.findExampleDataFile(\"Plaza2_DR.txt\")\n",
    "for row in np.loadtxt(data_file):\n",
    "    t, distance_traveled, delta_heading = row\n",
    "    odometry[t] = Pose2(distance_traveled, 0, delta_heading)\n",
    "M = len(odometry)\n",
    "print(f\"Read {M} odometry entries.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 1816 range triples.\n"
     ]
    }
   ],
   "source": [
    "# load the ranges from TD\n",
    "#    Time (sec)  Sender / Antenna ID Receiver Node ID  Range (m)\n",
    "triples = []\n",
    "data_file = gtsam.findExampleDataFile(\"Plaza2_TD.txt\")\n",
    "for row in np.loadtxt(data_file):\n",
    "    t, sender, receiver, _range = row\n",
    "    triples.append((t, int(receiver), _range))\n",
    "K = len(triples)\n",
    "print(f\"Read {K} range triples.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "minK = 150  # minimum number of range measurements to process initially\n",
    "incK = 25  # minimum number of range measurements to process after\n",
    "robust = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Noise parameters\n",
    "priorSigmas = gtsam.Point3(1, 1, math.pi)\n",
    "odoSigmas = gtsam.Point3(0.05, 0.01, 0.1)\n",
    "sigmaR = 100        # range standard deviation\n",
    "\n",
    "priorNoise = NM.Diagonal.Sigmas(priorSigmas)  # prior\n",
    "looseNoise = NM.Isotropic.Sigma(2, 1000)     # loose LM prior\n",
    "odoNoise = NM.Diagonal.Sigmas(odoSigmas)     # odometry\n",
    "gaussian = NM.Isotropic.Sigma(1, sigmaR)     # non-robust\n",
    "tukey = NM.Robust.Create(NM.mEstimator.Tukey.Create(15), gaussian)  # robust\n",
    "rangeNoise = tukey if robust else gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": cliques: 0, variables: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize iSAM\n",
    "isam = gtsam.ISAM2()\n",
    "print(isam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NonlinearFactorGraph: size: 1\n",
      "\n",
      "Factor 0: PriorFactor on 0\n",
      "  prior mean:  (-34.208649, 45.300764, 1.12050365)\n",
      "  noise model: diagonal sigmas[1; 1; 3.14159265];\n",
      "\n",
      " Values with 1 values:\n",
      "Value 0: (gtsam::Pose2)\n",
      "(-34.208649, 45.300764, 1.12050365)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add prior on first pose\n",
    "pose0 = Pose2(-34.2086489999201, 45.3007639991120, math.pi - 2.021089)\n",
    "newFactors = gtsam.NonlinearFactorGraph()\n",
    "newFactors.addPriorPose2(0, pose0, priorNoise)\n",
    "initial = gtsam.Values()\n",
    "initial.insert(0, pose0)\n",
    "print(newFactors, initial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding landmark L1\n",
      "Adding landmark L6\n",
      "Adding landmark L0\n",
      "Adding landmark L5\n",
      "Initializing at time 151\n"
     ]
    }
   ],
   "source": [
    "# set some loop variables\n",
    "i = 1  # step counter\n",
    "k = 0  # range measurement counter\n",
    "initialized = False\n",
    "lastPose = pose0\n",
    "countK = 0\n",
    "\n",
    "initializedLandmarks = set()\n",
    "\n",
    "# Loop over odometry\n",
    "for t, relative_pose in odometry.items():\n",
    "    # add odometry factor\n",
    "    newFactors.add(gtsam.BetweenFactorPose2(i - 1, i, relative_pose,\n",
    "                                            odoNoise))\n",
    "\n",
    "    # predict pose and add as initial estimate\n",
    "    predictedPose = lastPose.compose(relative_pose)\n",
    "    lastPose = predictedPose\n",
    "    initial.insert(i, predictedPose)\n",
    "\n",
    "    # Check if there are range factors to be added\n",
    "    while (k < K) and (triples[k][0] <= t):\n",
    "        j = triples[k][1]\n",
    "        landmark_key = gtsam.symbol('L', j)\n",
    "        _range = triples[k][2]\n",
    "        newFactors.add(gtsam.RangeFactor2D(\n",
    "            i, landmark_key, _range, rangeNoise))\n",
    "        if landmark_key not in initializedLandmarks:\n",
    "            p = rng.normal(loc=0, scale=100, size=(2,))\n",
    "            initial.insert(landmark_key, p)\n",
    "            print(f\"Adding landmark L{j}\")\n",
    "            initializedLandmarks.add(landmark_key)\n",
    "            # We also add a very loose prior on the landmark in case there is only\n",
    "            # one sighting, which cannot fully determine the landmark.\n",
    "            newFactors.add(gtsam.PriorFactorPoint2(\n",
    "                landmark_key, Point2(0, 0), looseNoise))\n",
    "        k = k + 1\n",
    "        countK = countK + 1\n",
    "\n",
    "    # Check whether to update iSAM 2\n",
    "    if (k > minK) and (countK > incK):\n",
    "        if not initialized:  # Do a full optimize for first minK ranges\n",
    "            print(f\"Initializing at time {k}\")\n",
    "            batchOptimizer = gtsam.LevenbergMarquardtOptimizer(\n",
    "                newFactors, initial)\n",
    "            initial = batchOptimizer.optimize()\n",
    "            initialized = True\n",
    "\n",
    "        isam.update(newFactors, initial)\n",
    "        result = isam.calculateEstimate()\n",
    "        lastPose = result.atPose2(i)\n",
    "        newFactors = gtsam.NonlinearFactorGraph()\n",
    "        initial = gtsam.Values()\n",
    "        countK = 0\n",
    "\n",
    "    i += 1\n",
    "\n",
    "finalResult = isam.calculateEstimate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5476377146882523136: [-35.97329685  26.31658086]\n",
      "5476377146882523137: [-75.1003452   21.01144091]\n",
      "5476377146882523141: [ -1.03876425 -12.13811931]\n",
      "5476377146882523142: [-36.08926944  72.3500464 ]\n"
     ]
    }
   ],
   "source": [
    "# Print optimized landmarks:\n",
    "for j in [0,1,5,6]:\n",
    "    landmark_key = gtsam.symbol('L', j)\n",
    "    p = finalResult.atPoint2(landmark_key)\n",
    "    print(f\"{landmark_key}: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4090, 2)\n"
     ]
    }
   ],
   "source": [
    "# plot positions\n",
    "poses = gtsam.utilities.allPose2s(finalResult)\n",
    "landmarks = gtsam.utilities.extractPoint2(finalResult)\n",
    "positions = np.array([poses.atPose2(key).translation()\n",
    "                     for key in poses.keys()])\n",
    "print(positions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "x"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "scaleanchor": "x",
         "scaleratio": 1,
         "title": {
          "text": "y"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(x=positions[:,0],y=positions[:,1])\n",
    "fig.add_scatter(x=landmarks[:,0], y=landmarks[:,1], mode=\"markers\", showlegend= False)\n",
    "fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))\n",
    "fig.update_yaxes(scaleanchor = \"x\", scaleratio = 1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-35.97329685,  26.31658086],\n",
       "       [-75.1003452 ,  21.01144091],\n",
       "       [ -1.03876425, -12.13811931],\n",
       "       [-36.08926944,  72.3500464 ]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmarks"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "341996cd3f3db7b5e0d1eaea072c5502d80452314e72e6b77c40445f6e9ba101"
  },
  "kernelspec": {
   "display_name": "Python 3.8.12 ('nbdev')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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