154 lines
5.2 KiB
C++
154 lines
5.2 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 SmartProjectionFactorExample.cpp
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* @brief A stereo visual odometry example
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* @date May 30, 2014
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* @author Stephen Camp
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* @author Chris Beall
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*/
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/**
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* A smart projection factor example based on stereo data, throwing away the
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* measurement from the right camera
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* -robot starts at origin
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* -moves forward, taking periodic stereo measurements
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* -makes monocular observations of many landmarks
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*/
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Cal3_S2Stereo.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/nonlinear/NonlinearEquality.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/slam/dataset.h>
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#include <gtsam/slam/SmartStereoProjectionPoseFactor.h>
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#include <string>
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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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int main(int argc, char** argv){
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typedef SmartStereoProjectionPoseFactor<Pose3, Point3, Cal3_S2Stereo> SmartFactor;
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bool output_poses = true;
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bool output_initial_poses = true;
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string poseOutput("../../../examples/data/optimized_poses.txt");
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string init_poseOutput("../../../examples/data/initial_poses.txt");
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Values initial_estimate;
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NonlinearFactorGraph graph;
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const noiseModel::Isotropic::shared_ptr model = noiseModel::Isotropic::Sigma(3,1);
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ofstream pose3Out, init_pose3Out;
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bool add_initial_noise = true;
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string calibration_loc = findExampleDataFile("VO_calibration.txt");
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string pose_loc = findExampleDataFile("VO_camera_poses_large.txt");
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string factor_loc = findExampleDataFile("VO_stereo_factors_large.txt");
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//read camera calibration info from file
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// focal lengths fx, fy, skew s, principal point u0, v0, baseline b
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cout << "Reading calibration info" << endl;
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ifstream calibration_file(calibration_loc.c_str());
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double fx, fy, s, u0, v0, b;
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calibration_file >> fx >> fy >> s >> u0 >> v0 >> b;
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const Cal3_S2Stereo::shared_ptr K(new Cal3_S2Stereo(fx, fy, s, u0, v0,b));
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cout << "Reading camera poses" << endl;
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ifstream pose_file(pose_loc.c_str());
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int pose_id;
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MatrixRowMajor m(4,4);
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//read camera pose parameters and use to make initial estimates of camera poses
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while (pose_file >> pose_id) {
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for (int i = 0; i < 16; i++) {
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pose_file >> m.data()[i];
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}
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if(add_initial_noise){
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m(1,3) += (pose_id % 10)/10.0;
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}
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initial_estimate.insert(Symbol('x', pose_id), Pose3(m));
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}
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Values initial_pose_values = initial_estimate.filter<Pose3>();
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if(output_poses){
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init_pose3Out.open(init_poseOutput.c_str(),ios::out);
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for(int i = 1; i<=initial_pose_values.size(); i++){
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init_pose3Out << i << " " << initial_pose_values.at<Pose3>(Symbol('x',i)).matrix().format(Eigen::IOFormat(Eigen::StreamPrecision, 0,
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" ", " ")) << endl;
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}
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}
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// camera and landmark keys
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size_t x, l;
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// pixel coordinates uL, uR, v (same for left/right images due to rectification)
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// landmark coordinates X, Y, Z in camera frame, resulting from triangulation
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double uL, uR, v, X, Y, Z;
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ifstream factor_file(factor_loc.c_str());
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cout << "Reading stereo factors" << endl;
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//read stereo measurements and construct smart factors
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SmartFactor::shared_ptr factor(new SmartFactor());
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size_t current_l = 3; // hardcoded landmark ID from first measurement
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while (factor_file >> x >> l >> uL >> uR >> v >> X >> Y >> Z) {
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if(current_l != l) {
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graph.push_back(factor);
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factor = SmartFactor::shared_ptr(new SmartFactor());
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current_l = l;
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}
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factor->add(StereoPoint2(uL,uR,v), Symbol('x',x), model, K);
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}
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Pose3 first_pose = initial_estimate.at<Pose3>(Symbol('x',1));
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//constrain the first pose such that it cannot change from its original value during optimization
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// NOTE: NonlinearEquality forces the optimizer to use QR rather than Cholesky
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// QR is much slower than Cholesky, but numerically more stable
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graph.push_back(NonlinearEquality<Pose3>(Symbol('x',1),first_pose));
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LevenbergMarquardtParams params;
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params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
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params.verbosity = NonlinearOptimizerParams::ERROR;
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cout << "Optimizing" << endl;
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//create Levenberg-Marquardt optimizer to optimize the factor graph
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LevenbergMarquardtOptimizer optimizer = LevenbergMarquardtOptimizer(graph, initial_estimate, params);
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Values result = optimizer.optimize();
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cout << "Final result sample:" << endl;
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Values pose_values = result.filter<Pose3>();
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pose_values.print("Final camera poses:\n");
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if(output_poses){
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pose3Out.open(poseOutput.c_str(),ios::out);
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for(int i = 1; i<=pose_values.size(); i++){
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pose3Out << i << " " << pose_values.at<Pose3>(Symbol('x',i)).matrix().format(Eigen::IOFormat(Eigen::StreamPrecision, 0,
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" ", " ")) << endl;
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}
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cout << "Writing output" << endl;
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}
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return 0;
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}
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