115 lines
		
	
	
		
			4.2 KiB
		
	
	
	
		
			C++
		
	
	
			
		
		
	
	
			115 lines
		
	
	
		
			4.2 KiB
		
	
	
	
		
			C++
		
	
	
| /* ----------------------------------------------------------------------------
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| 
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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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| 
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| * See LICENSE for the license information
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| 
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| * -------------------------------------------------------------------------- */
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| 
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| /**
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| * @file SteroVOExample.cpp
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| * @brief A stereo visual odometry example
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| * @date May 25, 2014
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| * @author Stephen Camp
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| */
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| 
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| 
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| /**
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|  * A 3D stereo visual odometry example
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|  *  - robot starts at origin
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|  *  -moves forward, taking periodic stereo measurements
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|  *  -takes stereo readings of many landmarks
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|  */
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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/StereoFactor.h>
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| #include <gtsam/slam/dataset.h>
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| 
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| #include <string>
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| #include <fstream>
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| #include <iostream>
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| 
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| using namespace std;
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| using namespace gtsam;
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| 
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| int main(int argc, char** argv){
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| 
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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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| 
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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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|   
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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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|   double fx, fy, s, u0, v0, b;
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|   ifstream calibration_file(calibration_loc.c_str());
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|   cout << "Reading calibration info" << endl;
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|   calibration_file >> fx >> fy >> s >> u0 >> v0 >> b;
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| 
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|   //create stereo camera calibration object
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|   const Cal3_S2Stereo::shared_ptr K(new Cal3_S2Stereo(fx,fy,s,u0,v0,b));
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|   
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|   ifstream pose_file(pose_loc.c_str());
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|   cout << "Reading camera poses" << endl;
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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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|     initial_estimate.insert(Symbol('x', pose_id), Pose3(m));
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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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| 
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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 measurement details from file and use to create and add GenericStereoFactor objects to the graph representation
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|   while (factor_file >> x >> l >> uL >> uR >> v >> X >> Y >> Z) {
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|     graph.push_back(
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|         GenericStereoFactor<Pose3, Point3>(StereoPoint2(uL, uR, v), model,
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|             Symbol('x', x), Symbol('l', l), K));
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|     //if the landmark variable included in this factor has not yet been added to the initial variable value estimate, add it
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|     if (!initial_estimate.exists(Symbol('l', l))) {
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|       Pose3 camPose = initial_estimate.at<Pose3>(Symbol('x', x));
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|       //transform_from() transforms the input Point3 from the camera pose space, camPose, to the global space
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|       Point3 worldPoint = camPose.transform_from(Point3(X, Y, Z));
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|       initial_estimate.insert(Symbol('l', l), worldPoint);
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|     }
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|   }
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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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| 
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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);
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|   Values result = optimizer.optimize();
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| 
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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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| 
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|   return 0;
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| }
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