208 lines
7.1 KiB
C++
208 lines
7.1 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file RangeISAMExample_plaza1.cpp
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* @brief A 2D Range SLAM example
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* @date June 20, 2013
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* @author FRank Dellaert
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*/
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// Both relative poses and recovered trajectory poses will be stored as Pose2 objects
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#include <gtsam/geometry/Pose2.h>
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// Each variable in the system (poses and landmarks) must be identified with a unique key.
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// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
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// Here we will use Symbols
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#include <gtsam/nonlinear/Symbol.h>
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// We want to use iSAM2 to solve the range-SLAM problem incrementally
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#include <gtsam/nonlinear/ISAM2.h>
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// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
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// and initial guesses for any new variables used in the added factors
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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// We will use a non-liear solver to batch-inituialize from the first 150 frames
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common factors
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// have been provided with the library for solving robotics SLAM problems.
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/slam/RangeFactor.h>
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// Standard headers, added last, so we know headers above work on their own
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#include <boost/foreach.hpp>
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#include <fstream>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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namespace NM = gtsam::noiseModel;
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// data available at http://www.frc.ri.cmu.edu/projects/emergencyresponse/RangeData/
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// Datafile format (from http://www.frc.ri.cmu.edu/projects/emergencyresponse/RangeData/log.html)
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// load the odometry
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// DR: Odometry Input (delta distance traveled and delta heading change)
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// Time (sec) Delta Dist. Trav. (m) Delta Heading (rad)
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typedef pair<double, Pose2> TimedOdometry;
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list<TimedOdometry> readOdometry() {
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list<TimedOdometry> odometryList;
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ifstream is("../../examples/Data/Plaza2_DR.txt");
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if (!is)
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throw runtime_error("../../examples/Data/Plaza2_DR.txt file not found");
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while (is) {
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double t, distance_traveled, delta_heading;
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is >> t >> distance_traveled >> delta_heading;
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odometryList.push_back(
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TimedOdometry(t, Pose2(distance_traveled, 0, delta_heading)));
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}
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is.clear(); /* clears the end-of-file and error flags */
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return odometryList;
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}
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// load the ranges from TD
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// Time (sec) Sender / Antenna ID Receiver Node ID Range (m)
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typedef boost::tuple<double, size_t, double> RangeTriple;
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vector<RangeTriple> readTriples() {
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vector<RangeTriple> triples;
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ifstream is("../../examples/Data/Plaza2_TD.txt");
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if (!is)
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throw runtime_error("../../examples/Data/Plaza2_TD.txt file not found");
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while (is) {
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double t, sender, receiver, range;
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is >> t >> sender >> receiver >> range;
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triples.push_back(RangeTriple(t, receiver, range));
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}
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is.clear(); /* clears the end-of-file and error flags */
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return triples;
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}
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// main
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int main (int argc, char** argv) {
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// load Plaza2 data
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list<TimedOdometry> odometry = readOdometry();
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// size_t M = odometry.size();
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vector<RangeTriple> triples = readTriples();
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size_t K = triples.size();
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// parameters
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size_t minK = 150; // minimum number of range measurements to process initially
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size_t incK = 25; // minimum number of range measurements to process after
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bool groundTruth = false;
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bool robust = true;
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// Set Noise parameters
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Vector priorSigmas = Vector3(1,1,M_PI);
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Vector odoSigmas = Vector3(0.05, 0.01, 0.2);
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double sigmaR = 100; // range standard deviation
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const NM::Base::shared_ptr // all same type
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priorNoise = NM::Diagonal::Sigmas(priorSigmas), //prior
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odoNoise = NM::Diagonal::Sigmas(odoSigmas), // odometry
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gaussian = NM::Isotropic::Sigma(1, sigmaR), // non-robust
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tukey = NM::Robust::Create(NM::mEstimator::Tukey::Create(15), gaussian), //robust
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rangeNoise = robust ? tukey : gaussian;
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// Initialize iSAM
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ISAM2 isam;
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// Add prior on first pose
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Pose2 pose0 = Pose2(-34.2086489999201, 45.3007639991120,
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M_PI - 2.02108900000000);
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NonlinearFactorGraph newFactors;
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newFactors.add(PriorFactor<Pose2>(0, pose0, priorNoise));
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Values initial;
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initial.insert(0, pose0);
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// initialize points
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if (groundTruth) { // from TL file
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initial.insert(symbol('L', 1), Point2(-68.9265, 18.3778));
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initial.insert(symbol('L', 6), Point2(-37.5805, 69.2278));
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initial.insert(symbol('L', 0), Point2(-33.6205, 26.9678));
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initial.insert(symbol('L', 5), Point2(1.7095, -5.8122));
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} else { // drawn from sigma=1 Gaussian in matlab version
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initial.insert(symbol('L', 1), Point2(3.5784, 2.76944));
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initial.insert(symbol('L', 6), Point2(-1.34989, 3.03492));
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initial.insert(symbol('L', 0), Point2(0.725404, -0.0630549));
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initial.insert(symbol('L', 5), Point2(0.714743, -0.204966));
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}
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// set some loop variables
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size_t i = 1; // step counter
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size_t k = 0; // range measurement counter
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bool initialized = false;
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Pose2 lastPose = pose0;
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size_t countK = 0;
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// Loop over odometry
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gttic_(iSAM);
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BOOST_FOREACH(const TimedOdometry& timedOdometry, odometry) {
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//--------------------------------- odometry loop -----------------------------------------
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double t;
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Pose2 odometry;
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boost::tie(t, odometry) = timedOdometry;
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// add odometry factor
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newFactors.add(BetweenFactor<Pose2>(i-1, i, odometry,NM::Diagonal::Sigmas(odoSigmas)));
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// predict pose and add as initial estimate
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Pose2 predictedPose = lastPose.compose(odometry);
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lastPose = predictedPose;
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initial.insert(i, predictedPose);
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// Check if there are range factors to be added
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while (k < K && t >= boost::get<0>(triples[k])) {
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size_t j = boost::get<1>(triples[k]);
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double range = boost::get<2>(triples[k]);
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newFactors.add(RangeFactor<Pose2, Point2>(i, symbol('L', j), range,rangeNoise));
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k = k + 1;
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countK = countK + 1;
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}
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// Check whether to update iSAM 2
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if ((k > minK) && (countK > incK)) {
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if (!initialized) { // Do a full optimize for first minK ranges
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gttic_(batchInitialization);
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LevenbergMarquardtOptimizer batchOptimizer(newFactors, initial);
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initial = batchOptimizer.optimize();
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gttoc_(batchInitialization);
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initialized = true;
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}
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gttic_(update);
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isam.update(newFactors, initial);
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gttoc_(update);
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gttic_(calculateEstimate);
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Values result = isam.calculateEstimate();
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gttoc_(calculateEstimate);
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lastPose = result.at<Pose2>(i);
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newFactors = NonlinearFactorGraph();
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initial = Values();
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countK = 0;
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}
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i += 1;
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//--------------------------------- odometry loop -----------------------------------------
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} // BOOST_FOREACH
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gttoc_(iSAM);
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// Print timings
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tictoc_print_();
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exit(0);
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}
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