124 lines
4.1 KiB
C++
124 lines
4.1 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 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/slam/SmartProjectionPoseFactor.h>
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#include <gtsam/slam/dataset.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 <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 PinholePose<Cal3_S2> Camera;
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typedef SmartProjectionPoseFactor<Cal3_S2> SmartFactor;
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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(2,1);
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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_S2::shared_ptr K(new Cal3_S2(fx, fy, s, u0, v0));
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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_index;
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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_index) {
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for (int i = 0; i < 16; i++)
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pose_file >> m.data()[i];
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initial_estimate.insert(pose_index, Pose3(m));
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}
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// landmark keys
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size_t landmark_key;
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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(model, K));
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size_t current_l = 3; // hardcoded landmark ID from first measurement
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while (factor_file >> pose_index >> landmark_key >> uL >> uR >> v >> X >> Y >> Z) {
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if(current_l != landmark_key) {
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graph.push_back(factor);
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factor = SmartFactor::shared_ptr(new SmartFactor(model, K));
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current_l = landmark_key;
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}
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factor->add(Point2(uL,v), pose_index);
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}
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Pose3 firstPose = initial_estimate.at<Pose3>(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>(1,firstPose));
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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(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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return 0;
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}
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