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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SelfCalibrationExample.cpp
* @brief Based on VisualSLAMExample, but with unknown (yet fixed) calibration.
* @author Frank Dellaert
*/
/*
* See the detailed documentation in Visual SLAM.
* The only documentation below with deal with the self-calibration.
*/
// For loading the data
#include "SFMdata.h"
// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
#include <gtsam/geometry/Point2.h>
// Inference and optimization
#include <gtsam/inference/Symbol.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/DoglegOptimizer.h>
#include <gtsam/nonlinear/Values.h>
// SFM-specific factors
#include <gtsam/slam/GeneralSFMFactor.h> // does calibration !
// Standard headers
#include <vector>
using namespace std;
using namespace gtsam;
int main(int argc, char* argv[]) {
// Create the set of ground-truth
vector<Point3> points = createPoints();
vector<Pose3> poses = createPoses();
// Create the factor graph
NonlinearFactorGraph graph;
// Add a prior on pose x1.
auto poseNoise = noiseModel::Diagonal::Sigmas(
(Vector(6) << Vector3::Constant(0.1), Vector3::Constant(0.3))
.finished()); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
graph.addPrior(Symbol('x', 0), poses[0], poseNoise);
// Simulated measurements from each camera pose, adding them to the factor
// graph
Cal3_S2 K(50.0, 50.0, 0.0, 50.0, 50.0);
auto measurementNoise =
noiseModel::Isotropic::Sigma(2, 1.0);
for (size_t i = 0; i < poses.size(); ++i) {
for (size_t j = 0; j < points.size(); ++j) {
PinholeCamera<Cal3_S2> camera(poses[i], K);
Point2 measurement = camera.project(points[j]);
// The only real difference with the Visual SLAM example is that here we
// use a different factor type, that also calculates the Jacobian with
// respect to calibration
graph.emplace_shared<GeneralSFMFactor2<Cal3_S2> >(
measurement, measurementNoise, Symbol('x', i), Symbol('l', j),
Symbol('K', 0));
}
}
// Add a prior on the position of the first landmark.
auto pointNoise =
noiseModel::Isotropic::Sigma(3, 0.1);
graph.addPrior(Symbol('l', 0), points[0],
pointNoise); // add directly to graph
// Add a prior on the calibration.
auto calNoise = noiseModel::Diagonal::Sigmas(
(Vector(5) << 500, 500, 0.1, 100, 100).finished());
graph.addPrior(Symbol('K', 0), K, calNoise);
// Create the initial estimate to the solution
// now including an estimate on the camera calibration parameters
Values initialEstimate;
initialEstimate.insert(Symbol('K', 0), Cal3_S2(60.0, 60.0, 0.0, 45.0, 45.0));
for (size_t i = 0; i < poses.size(); ++i)
initialEstimate.insert(
Symbol('x', i), poses[i].compose(Pose3(Rot3::Rodrigues(-0.1, 0.2, 0.25),
Point3(0.05, -0.10, 0.20))));
for (size_t j = 0; j < points.size(); ++j)
initialEstimate.insert<Point3>(Symbol('l', j),
points[j] + Point3(-0.25, 0.20, 0.15));
/* Optimize the graph and print results */
Values result = DoglegOptimizer(graph, initialEstimate).optimize();
result.print("Final results:\n");
return 0;
}
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