97 lines
3.7 KiB
C++
97 lines
3.7 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 SelfCalibrationExample.cpp
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* @brief Based on VisualSLAMExample, but with unknown (yet fixed) calibration.
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* @author Frank Dellaert
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*/
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/*
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* See the detailed documentation in Visual SLAM.
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* The only documentation below with deal with the self-calibration.
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*/
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// For loading the data
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#include "SFMdata.h"
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// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
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#include <gtsam/geometry/Point2.h>
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// Inference and optimization
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/DoglegOptimizer.h>
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#include <gtsam/nonlinear/Values.h>
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// SFM-specific factors
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/GeneralSFMFactor.h> // does calibration !
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// Standard headers
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#include <vector>
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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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// Create the set of ground-truth
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vector<Point3> points = createPoints();
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vector<Pose3> poses = createPoses();
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// Create the factor graph
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NonlinearFactorGraph graph;
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// Add a prior on pose x1.
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise));
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// Simulated measurements from each camera pose, adding them to the factor graph
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Cal3_S2 K(50.0, 50.0, 0.0, 50.0, 50.0);
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noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0);
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for (size_t i = 0; i < poses.size(); ++i) {
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for (size_t j = 0; j < points.size(); ++j) {
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SimpleCamera camera(poses[i], K);
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Point2 measurement = camera.project(points[j]);
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// The only real difference with the Visual SLAM example is that here we use a
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// different factor type, that also calculates the Jacobian with respect to calibration
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graph.push_back(GeneralSFMFactor2<Cal3_S2>(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), Symbol('K', 0)));
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}
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}
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// Add a prior on the position of the first landmark.
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noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
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graph.push_back(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
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// Add a prior on the calibration.
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noiseModel::Diagonal::shared_ptr calNoise = noiseModel::Diagonal::Sigmas((Vector(5) << 500, 500, 0.1, 100, 100));
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graph.push_back(PriorFactor<Cal3_S2>(Symbol('K', 0), K, calNoise));
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// Create the initial estimate to the solution
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// now including an estimate on the camera calibration parameters
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Values initialEstimate;
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initialEstimate.insert(Symbol('K', 0), Cal3_S2(60.0, 60.0, 0.0, 45.0, 45.0));
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for (size_t i = 0; i < poses.size(); ++i)
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initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
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for (size_t j = 0; j < points.size(); ++j)
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initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
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/* Optimize the graph and print results */
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Values result = DoglegOptimizer(graph, initialEstimate).optimize();
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result.print("Final results:\n");
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return 0;
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}
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/* ************************************************************************* */
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