023 gtsam/examples/RangeISAMExample_plaza2.cpp

一、说明和include files

// Both relative poses and recovered trajectory poses will be stored as Pose2 objects
#include 

// 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 

// We want to use iSAM2 to solve the range-SLAM problem incrementally
#include 

// 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 
#include 

// We will use a non-liear solver to batch-inituialize from the first 150 frames
#include 

// In GTSAM, measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics SLAM problems.
#include 
#include 
#include 

// Standard headers, added last, so we know headers above work on their own
#include 
#include 

二、加载数据集

// 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 data_file = findExampleDataFile("Plaza2_DR.txt");
  ifstream is(data_file.c_str());

  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 boost::tuple<double, size_t, double> RangeTriple;
vector<RangeTriple> readTriples() {
  vector<RangeTriple> triples;
  string data_file = findExampleDataFile("Plaza2_TD.txt");
  ifstream is(data_file.c_str());

  while (is) {
    double t, sender, range;
    size_t receiver;
    is >> t >> sender >> receiver >> range;
    triples.push_back(RangeTriple(t, receiver, range));
  }
  is.clear(); /* clears the end-of-file and error flags */
  return triples;
}

数据读取和参数设置

  // load Plaza2 data
  list<TimedOdometry> odometry = readOdometry();
//  size_t M = odometry.size();

  vector<RangeTriple> triples = readTriples();
  size_t K = triples.size();

  // parameters
  size_t minK = 150; // minimum number of range measurements to process initially
  size_t incK = 25; // minimum number of range measurements to process after
  bool groundTruth = false;
  bool robust = true;

  // Set Noise parameters
  Vector priorSigmas = Vector3(1,1,M_PI);
  Vector odoSigmas = Vector3(0.05, 0.01, 0.1);
  double sigmaR = 100; // range standard deviation
  const NM::Base::shared_ptr // all same type
  priorNoise = NM::Diagonal::Sigmas(priorSigmas), //prior
  odoNoise = NM::Diagonal::Sigmas(odoSigmas), // odometry
  gaussian = NM::Isotropic::Sigma(1, sigmaR), // non-robust
  tukey = NM::Robust::Create(NM::mEstimator::Tukey::Create(15), gaussian), //robust
  rangeNoise = robust ? tukey : gaussian;

robust kernel
　
初始化iSAM和添加先验

  // 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);
  Values initial;
  initial.insert(0, pose0);

landmarks初始化

  //  initialize points
  if (groundTruth) { // from TL file
    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));
  } else { // drawn from sigma=1 Gaussian in matlab version
    initial.insert(symbol('L', 1), Point2(3.5784, 2.76944));
    initial.insert(symbol('L', 6), Point2(-1.34989, 3.03492));
    initial.insert(symbol('L', 0), Point2(0.725404, -0.0630549));
    initial.insert(symbol('L', 5), Point2(0.714743, -0.204966));
  }

  // Loop over odometry
  gttic_(iSAM);
  for(const TimedOdometry& timedOdometry: odometry) {
    //--------------------------------- odometry loop -----------------------------------------
    double t;
    Pose2 odometry;
    boost::tie(t, odometry) = timedOdometry;

    // add odometry factor //添加里程计因子
    newFactors.push_back(BetweenFactor<Pose2>(i-1, i, odometry, odoNoise));

    // predict pose and add as initial estimate //预测估计值
    Pose2 predictedPose = lastPose.compose(odometry);
    lastPose = predictedPose;
    initial.insert(i, predictedPose);

    // Check if there are range factors to be added
    // 添加RangeFactor
    while (k < K && t >= boost::get<0>(triples[k])) {
      size_t j = boost::get<1>(triples[k]);
      double range = boost::get<2>(triples[k]);
      newFactors.push_back(RangeFactor<Pose2, Point2>(i, symbol('L', j), range,rangeNoise));
      k = k + 1;
      countK = countK + 1;
    }

    // Check whether to update iSAM 2
    // 更新iSAM2
    if ((k > minK) && (countK > incK)) {
      if (!initialized) { // Do a full optimize for first minK ranges
        gttic_(batchInitialization);
        LevenbergMarquardtOptimizer batchOptimizer(newFactors, initial);
        initial = batchOptimizer.optimize();
        gttoc_(batchInitialization);
        initialized = true;
      }
      gttic_(update);
      isam.update(newFactors, initial);
      gttoc_(update);
      gttic_(calculateEstimate);
      Values result = isam.calculateEstimate();
      gttoc_(calculateEstimate);
      lastPose = result.at<Pose2>(i);
      newFactors = NonlinearFactorGraph();
      initial = Values();
      countK = 0;
    }
    i += 1;
    //--------------------------------- odometry loop -----------------------------------------
  } // end for