239 lines
8.3 KiB
C++
239 lines
8.3 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_plaza2.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.
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#include <gtsam/geometry/Pose2.h>
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using gtsam::Pose2;
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// gtsam::Vectors are dynamic Eigen vectors, Vector3 is statically sized.
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#include <gtsam/base/Vector.h>
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using gtsam::Vector;
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using gtsam::Vector3;
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// Unknown landmarks are of type Point2 (which is just a 2D Eigen vector).
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#include <gtsam/geometry/Point2.h>
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using gtsam::Point2;
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// Each variable in the system (poses and landmarks) must be identified with a
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// unique key. We can either use simple integer keys (1, 2, 3, ...) or symbols
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// (X1, X2, L1). Here we will use Symbols.
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#include <gtsam/inference/Symbol.h>
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using gtsam::Symbol;
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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
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// factor graph, and initial guesses for any new variables 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-linear solver to batch-initialize from the first 150 frames
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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// 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/sam/RangeFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/slam/dataset.h>
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// Timing, with functions below, provides nice facilities to benchmark.
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#include <gtsam/base/timing.h>
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using gtsam::tictoc_print_;
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// Standard headers, added last, so we know headers above work on their own.
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#include <fstream>
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#include <iostream>
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#include <random>
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#include <set>
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#include <string>
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#include <utility>
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#include <vector>
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namespace NM = gtsam::noiseModel;
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// Data is second UWB ranging dataset, B2 or "plaza 2", from
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// "Navigating with Ranging Radios: Five Data Sets with Ground Truth"
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// by Joseph Djugash, Bradley Hamner, and Stephan Roth
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// https://www.ri.cmu.edu/pub_files/2009/9/Final_5datasetsRangingRadios.pdf
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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 Distance Traveled (m) Delta Heading (rad)
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using TimedOdometry = std::pair<double, Pose2>;
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std::list<TimedOdometry> readOdometry() {
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std::list<TimedOdometry> odometryList;
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std::string data_file = gtsam::findExampleDataFile("Plaza2_DR.txt");
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std::ifstream is(data_file.c_str());
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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.emplace_back(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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using RangeTriple = std::tuple<double, size_t, double>;
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std::vector<RangeTriple> readTriples() {
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std::vector<RangeTriple> triples;
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std::string data_file = gtsam::findExampleDataFile("Plaza2_TD.txt");
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std::ifstream is(data_file.c_str());
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while (is) {
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double t, range, sender, receiver;
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is >> t >> sender >> receiver >> range;
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triples.emplace_back(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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std::list<TimedOdometry> odometry = readOdometry();
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size_t M = odometry.size();
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std::cout << "Read " << M << " odometry entries." << std::endl;
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std::vector<RangeTriple> triples = readTriples();
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size_t K = triples.size();
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std::cout << "Read " << K << " range triples." << std::endl;
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// parameters
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size_t minK =
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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 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, 0.1);
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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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looseNoise = NM::Isotropic::Sigma(2, 1000), // loose LM 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),
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gaussian), // robust
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rangeNoise = robust ? tukey : gaussian;
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// Initialize iSAM
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gtsam::ISAM2 isam;
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// Add prior on first pose
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Pose2 pose0 = Pose2(-34.2086489999201, 45.3007639991120, M_PI - 2.021089);
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gtsam::NonlinearFactorGraph newFactors;
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newFactors.addPrior(0, pose0, priorNoise);
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gtsam::Values initial;
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initial.insert(0, pose0);
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// We will initialize landmarks randomly, and keep track of which landmarks we
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// already added with a set.
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std::mt19937_64 rng;
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std::normal_distribution<double> normal(0.0, 100.0);
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std::set<Symbol> initializedLandmarks;
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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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for (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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std::tie(t, odometry) = timedOdometry;
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// add odometry factor
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newFactors.emplace_shared<gtsam::BetweenFactor<Pose2>>(i - 1, i, odometry,
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odoNoise);
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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 >= std::get<0>(triples[k])) {
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size_t j = std::get<1>(triples[k]);
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Symbol landmark_key('L', j);
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double range = std::get<2>(triples[k]);
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newFactors.emplace_shared<gtsam::RangeFactor<Pose2, Point2>>(
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i, landmark_key, range, rangeNoise);
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if (initializedLandmarks.count(landmark_key) == 0) {
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std::cout << "adding landmark " << j << std::endl;
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double x = normal(rng), y = normal(rng);
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initial.insert(landmark_key, Point2(x, y));
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initializedLandmarks.insert(landmark_key);
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// We also add a very loose prior on the landmark in case there is only
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// one sighting, which cannot fully determine the landmark.
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newFactors.emplace_shared<gtsam::PriorFactor<Point2>>(
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landmark_key, Point2(0, 0), looseNoise);
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}
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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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std::cout << "Initializing at time " << k << std::endl;
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gttic_(batchInitialization);
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gtsam::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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gtsam::Values result = isam.calculateEstimate();
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gttoc_(calculateEstimate);
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lastPose = result.at<Pose2>(i);
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newFactors = gtsam::NonlinearFactorGraph();
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initial = gtsam::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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} // end for
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gttoc_(iSAM);
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// Print timings
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tictoc_print_();
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// Print optimized landmarks:
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gtsam::Values finalResult = isam.calculateEstimate();
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for (auto&& landmark_key : initializedLandmarks) {
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Point2 p = finalResult.at<Point2>(landmark_key);
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std::cout << landmark_key << ":" << p.transpose() << "\n";
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}
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exit(0);
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}
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