SmartRangeExample_plaza1.cpp

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/* ----------------------------------------------------------------------------
 
 * GTSAM Copyright 2010, Georgia Tech Research Corporation,
 * Atlanta, Georgia 30332-0415
 * All Rights Reserved
 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
 
 * See LICENSE for the license information
 
 * -------------------------------------------------------------------------- */
 
// Both relative poses and recovered trajectory poses will be stored as Pose2 objects
#include <gtsam/geometry/Pose2.h>
 
// Each variable in the system (poses and landmarks) must be identified with a unique key.
// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
// Here we will use Symbols
#include <gtsam/inference/Symbol.h>
 
// We want to use iSAM2 to solve the range-SLAM problem incrementally
#include <gtsam/nonlinear/ISAM2.h>
 
// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
// and initial guesses for any new variables used in the added factors
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
 
// We will use a non-liear solver to batch-inituialize from the first 150 frames
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
 
// In GTSAM, measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics SLAM problems.
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/sam/RangeFactor.h>
#include <gtsam_unstable/slam/SmartRangeFactor.h>
 
// To find data files, we can use `findExampleDataFile`, declared here:
#include <gtsam/slam/dataset.h>
 
// Standard headers, added last, so we know headers above work on their own
#include <fstream>
#include <iostream>
 
using namespace std;
using namespace gtsam;
namespace NM = gtsam::noiseModel;
 
// data available at http://www.frc.ri.cmu.edu/projects/emergencyresponse/RangeData/
// Datafile format (from http://www.frc.ri.cmu.edu/projects/emergencyresponse/RangeData/log.html)
 
// load the odometry
// DR: Odometry Input (delta distance traveled and delta heading change)
//    Time (sec)  Delta Dist. Trav. (m) Delta Heading (rad)
typedef pair<double, Pose2> TimedOdometry;
list<TimedOdometry> readOdometry() {
  list<TimedOdometry> odometryList;
  string drFile = findExampleDataFile("Plaza1_DR.txt");
  ifstream is(drFile);
  if (!is) throw runtime_error("Plaza1_DR.txt file not found");
 
  while (is) {
    double t, distance_traveled, delta_heading;
    is >> t >> distance_traveled >> delta_heading;
    odometryList.push_back(
        TimedOdometry(t, Pose2(distance_traveled, 0, delta_heading)));
  }
  is.clear(); /* clears the end-of-file and error flags */
  return odometryList;
}
 
// load the ranges from TD
//    Time (sec)  Sender / Antenna ID Receiver Node ID  Range (m)
typedef std::tuple<double, size_t, double> RangeTriple;
vector<RangeTriple> readTriples() {
  vector<RangeTriple> triples;
  string tdFile = findExampleDataFile("Plaza1_TD.txt");
  ifstream is(tdFile);
  if (!is) throw runtime_error("Plaza1_TD.txt file not found");
 
  while (is) {
    double t, sender, receiver, range;
    is >> t >> sender >> receiver >> range;
    triples.push_back(RangeTriple(t, receiver, range));
  }
  is.clear(); /* clears the end-of-file and error flags */
  return triples;
}
 
// main
int main(int argc, char** argv) {
 
  // load Plaza1 data
  list<TimedOdometry> odometry = readOdometry();
  vector<RangeTriple> triples = readTriples();
  size_t K = triples.size();
 
  // parameters
  size_t start = 220, end=3000;
  size_t minK = 100; // first batch of smart factors
  size_t incK = 50; // minimum number of range measurements to process after
  bool robust = true;
  bool smart = true;
 
  // Set Noise parameters
  Vector priorSigmas = Vector3(1, 1, M_PI);
  Vector odoSigmas = Vector3(0.05, 0.01, 0.1);
  auto odoNoise = NM::Diagonal::Sigmas(odoSigmas);
  double sigmaR = 100; // range standard deviation
  const NM::Base::shared_ptr // all same type
  priorNoise = NM::Diagonal::Sigmas(priorSigmas), //prior
  gaussian = NM::Isotropic::Sigma(1, sigmaR), // non-robust
  tukey = NM::Robust::Create(NM::mEstimator::Tukey::Create(15), gaussian), //robust
  rangeNoise = robust ? tukey : gaussian;
 
  // Initialize iSAM
  ISAM2 isam;
 
  // Add prior on first pose
  Pose2 pose0 = Pose2(-34.2086489999201, 45.3007639991120,
      M_PI - 2.02108900000000);
  NonlinearFactorGraph newFactors;
  newFactors.addPrior(0, pose0, priorNoise);
 
  ofstream os2("rangeResultLM.txt");
  ofstream os3("rangeResultSR.txt");
 
  //  initialize points (Gaussian)
  Values initial;
  if (!smart) {
    initial.insert(symbol('L', 1), Point2(-68.9265, 18.3778));
    initial.insert(symbol('L', 6), Point2(-37.5805, 69.2278));
    initial.insert(symbol('L', 0), Point2(-33.6205, 26.9678));
    initial.insert(symbol('L', 5), Point2(1.7095, -5.8122));
  }
  Values landmarkEstimates = initial; // copy landmarks
  initial.insert(0, pose0);
 
  //  initialize smart range factors
  size_t ids[] = { 1, 6, 0, 5 };
  typedef std::shared_ptr<SmartRangeFactor> SmartPtr;
  map<size_t, SmartPtr> smartFactors;
  if (smart) {
    for(size_t jj: ids) {
      smartFactors[jj] = SmartPtr(new SmartRangeFactor(sigmaR));
      newFactors.push_back(smartFactors[jj]);
    }
  }
 
  // set some loop variables
  size_t i = 1; // step counter
  size_t k = 0; // range measurement counter
  Pose2 lastPose = pose0;
  size_t countK = 0, totalCount=0;
 
  // Loop over odometry
  gttic_(iSAM);
  for(const TimedOdometry& timedOdometry: odometry) {
    //--------------------------------- odometry loop -----------------------------------------
    double t;
    Pose2 odometry;
    std::tie(t, odometry) = timedOdometry;
    printf("step %d, time = %g\n",(int)i,t);
 
    // add odometry factor
    newFactors.push_back(
        BetweenFactor<Pose2>(i - 1, i, odometry, odoNoise));
 
    // predict pose and add as initial estimate
    Pose2 predictedPose = lastPose.compose(odometry);
    lastPose = predictedPose;
    initial.insert(i, predictedPose);
    landmarkEstimates.insert(i, predictedPose);
 
    // Check if there are range factors to be added
    while (k < K && t >= std::get<0>(triples[k])) {
      size_t j = std::get<1>(triples[k]);
      double range = std::get<2>(triples[k]);
      if (i > start) {
        if (smart && totalCount < minK) {
          try {
            smartFactors[j]->addRange(i, range);
            printf("adding range %g for %d",range,(int)j);
          } catch (const invalid_argument& e) {
            printf("warning: omitting duplicate range %g for %d: %s", range,
                   (int)j, e.what());
          }
          cout << endl;
        }
        else {
          RangeFactor<Pose2, Point2> factor(i, symbol('L', j), range,
              rangeNoise);
          // Throw out obvious outliers based on current landmark estimates
          Vector error = factor.unwhitenedError(landmarkEstimates);
          if (k <= 200 || std::abs(error[0]) < 5)
            newFactors.push_back(factor);
        }
        totalCount += 1;
      }
      k = k + 1;
      countK = countK + 1;
    }
 
    // Check whether to update iSAM 2
    if (k >= minK && countK >= incK) {
      gttic_(update);
      isam.update(newFactors, initial);
      gttoc_(update);
      gttic_(calculateEstimate);
      Values result = isam.calculateEstimate();
      gttoc_(calculateEstimate);
      lastPose = result.at<Pose2>(i);
      bool hasLandmarks = result.exists(symbol('L', ids[0]));
      if (hasLandmarks) {
        // update landmark estimates
        landmarkEstimates = Values();
        for(size_t jj: ids)
          landmarkEstimates.insert(symbol('L', jj), result.at(symbol('L', jj)));
      }
      newFactors = NonlinearFactorGraph();
      initial = Values();
      if (smart && !hasLandmarks) {
        cout << "initialize from smart landmarks" << endl;
        for(size_t jj: ids) {
          Point2 landmark = smartFactors[jj]->triangulate(result);
          initial.insert(symbol('L', jj), landmark);
          landmarkEstimates.insert(symbol('L', jj), landmark);
        }
      }
      countK = 0;
      for (const auto& [key, point] : result.extract<Point2>())
        os2 << key << "\t" << point.x() << "\t" << point.y() << "\t1" << endl;
      if (smart) {
        for(size_t jj: ids) {
          Point2 landmark = smartFactors[jj]->triangulate(result);
          os3 << jj << "\t" << landmark.x() << "\t" << landmark.y() << "\t1"
              << endl;
        }
      }
    }
    i += 1;
    if (i>end) break;
    //--------------------------------- odometry loop -----------------------------------------
  } // end for
  gttoc_(iSAM);
 
  // Print timings
  tictoc_print_();
 
  // Write result to file
  Values result = isam.calculateEstimate();
  ofstream os("rangeResult.txt");
  for (const auto& [key, pose] : result.extract<Pose2>())
    os << key << "\t" << pose.x() << "\t" << pose.y() << "\t" << pose.theta() << endl;
  exit(0);
}