103 lines
3.5 KiB
C++
103 lines
3.5 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 CameraResectioning.cpp
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* @brief An example of gtsam for solving the camera resectioning problem
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* @author Duy-Nguyen Ta
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* @date Aug 23, 2011
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*/
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#include <gtsam/nonlinear/Symbol.h>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Cal3_S2.h>
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#include <gtsam/geometry/SimpleCamera.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/NonlinearOptimization.h>
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using namespace gtsam;
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/**
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* Unary factor for the pose.
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*/
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class ResectioningFactor: public NoiseModelFactor1<Pose3> {
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typedef NoiseModelFactor1<Pose3> Base;
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shared_ptrK K_; // camera's intrinsic parameters
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Point3 P_; // 3D point on the calibration rig
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Point2 p_; // 2D measurement of the 3D point
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public:
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ResectioningFactor(const SharedNoiseModel& model, const Symbol& key,
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const shared_ptrK& calib, const Point2& p, const Point3& P) :
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Base(model, key), K_(calib), P_(P), p_(p) {
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}
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virtual ~ResectioningFactor() {}
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virtual Vector evaluateError(const Pose3& X, boost::optional<Matrix&> H = boost::none) const {
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SimpleCamera camera(*K_, X);
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Point2 reprojectionError(camera.project(P_, H) - p_);
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return reprojectionError.vector();
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}
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};
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/*******************************************************************************/
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/**
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* Camera: f = 1.0, Image: 100x100, center: 50.0, 50.0
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* Pose (ground truth): (Xw, -Yw, -Zw, [0,0,2.0]')
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* Known landmarks:
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* 3D Points: (10,10,0) (-10,10,0) (-10,-10,0) (10,-10,0)
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* Perfect measurements:
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* 2D Point: (55,45) (45,45) (45,55) (55,55)
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*/
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int main(int argc, char* argv[]) {
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/* read camera intrinsic parameters */
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shared_ptrK calib(new Cal3_S2(1.0, 1.0, 0, 50.0, 50.0));
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/* create keys for variables */
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// we have only 1 variable to solve: the camera pose
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Symbol X('x',1);
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/* 1. create graph */
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NonlinearFactorGraph graph;
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/* 2. add factors to the graph */
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// add measurement factors
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SharedDiagonal measurementNoise = sharedSigmas(Vector_(2, 0.5, 0.5));
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boost::shared_ptr<ResectioningFactor> factor;
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factor = boost::shared_ptr<ResectioningFactor>(new ResectioningFactor(
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measurementNoise, X, calib, Point2(55.0, 45.0), Point3(10.0, 10.0, 0.0)));
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graph.push_back(factor);
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factor = boost::shared_ptr<ResectioningFactor>(new ResectioningFactor(
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measurementNoise, X, calib, Point2(45.0, 45.0), Point3(-10.0, 10.0, 0.0)));
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graph.push_back(factor);
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factor = boost::shared_ptr<ResectioningFactor>(new ResectioningFactor(
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measurementNoise, X, calib, Point2(45.0, 55.0), Point3(-10.0, -10.0, 0.0)));
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graph.push_back(factor);
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factor = boost::shared_ptr<ResectioningFactor>(new ResectioningFactor(
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measurementNoise, X, calib, Point2(55.0, 55.0), Point3(10.0, -10.0, 0.0)));
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graph.push_back(factor);
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/* 3. Create an initial estimate for the camera pose */
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Values initial;
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initial.insert(X, Pose3(Rot3(1.,0.,0.,
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0.,-1.,0.,
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0.,0.,-1.), Point3(0.,0.,2.0)));
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/* 4. Optimize the graph using Levenberg-Marquardt*/
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Values result = optimize<NonlinearFactorGraph> (graph, initial);
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result.print("Final result: ");
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return 0;
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}
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