133 lines
5.2 KiB
C++
133 lines
5.2 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 SFMExample_SmartFactor.cpp
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* @brief A structure-from-motion problem on a simulated dataset, using smart projection factor
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* @author Duy-Nguyen Ta
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* @author Jing Dong
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* @author Frank Dellaert
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*/
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// In GTSAM, measurement functions are represented as 'factors'.
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// The factor we used here is SmartProjectionPoseFactor.
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// Every smart factor represent a single landmark, seen from multiple cameras.
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// The SmartProjectionPoseFactor only optimizes for the poses of a camera,
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// not the calibration, which is assumed known.
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#include <gtsam/slam/SmartProjectionPoseFactor.h>
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// For an explanation of these headers, see SFMExample.cpp
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#include "SFMdata.h"
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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using namespace std;
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using namespace gtsam;
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// Make the typename short so it looks much cleaner
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typedef SmartProjectionPoseFactor<Cal3_S2> SmartFactor;
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// create a typedef to the camera type
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typedef PinholePose<Cal3_S2> Camera;
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/* ************************************************************************* */
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int main(int argc, char* argv[]) {
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// Define the camera calibration parameters
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Cal3_S2::shared_ptr K(new Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
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// Define the camera observation noise model
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noiseModel::Isotropic::shared_ptr measurementNoise =
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noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
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// Create the set of ground-truth landmarks and poses
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vector<Point3> points = createPoints();
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vector<Pose3> poses = createPoses();
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// Create a factor graph
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NonlinearFactorGraph graph;
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// Simulated measurements from each camera pose, adding them to the factor graph
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for (size_t j = 0; j < points.size(); ++j) {
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// every landmark represent a single landmark, we use shared pointer to init the factor, and then insert measurements.
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SmartFactor::shared_ptr smartfactor(new SmartFactor(measurementNoise, K));
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for (size_t i = 0; i < poses.size(); ++i) {
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// generate the 2D measurement
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Camera camera(poses[i], K);
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Point2 measurement = camera.project(points[j]);
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// call add() function to add measurement into a single factor, here we need to add:
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// 1. the 2D measurement
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// 2. the corresponding camera's key
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// 3. camera noise model
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// 4. camera calibration
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smartfactor->add(measurement, i);
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}
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// insert the smart factor in the graph
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graph.push_back(smartfactor);
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}
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// Add a prior on pose x0. This indirectly specifies where the origin is.
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// 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noiseModel::Diagonal::shared_ptr noise = noiseModel::Diagonal::Sigmas(
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(Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1)).finished());
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graph.emplace_shared<PriorFactor<Pose3> >(0, poses[0], noise);
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// Because the structure-from-motion problem has a scale ambiguity, the problem is
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// still under-constrained. Here we add a prior on the second pose x1, so this will
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// fix the scale by indicating the distance between x0 and x1.
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// Because these two are fixed, the rest of the poses will be also be fixed.
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graph.emplace_shared<PriorFactor<Pose3> >(1, poses[1], noise); // add directly to graph
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graph.print("Factor Graph:\n");
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// Create the initial estimate to the solution
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// Intentionally initialize the variables off from the ground truth
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Values initialEstimate;
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Pose3 delta(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
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for (size_t i = 0; i < poses.size(); ++i)
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initialEstimate.insert(i, poses[i].compose(delta));
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initialEstimate.print("Initial Estimates:\n");
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// Optimize the graph and print results
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LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
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Values result = optimizer.optimize();
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result.print("Final results:\n");
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// A smart factor represent the 3D point inside the factor, not as a variable.
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// The 3D position of the landmark is not explicitly calculated by the optimizer.
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// To obtain the landmark's 3D position, we use the "point" method of the smart factor.
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Values landmark_result;
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for (size_t j = 0; j < points.size(); ++j) {
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// The graph stores Factor shared_ptrs, so we cast back to a SmartFactor first
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SmartFactor::shared_ptr smart = boost::dynamic_pointer_cast<SmartFactor>(graph[j]);
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if (smart) {
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// The output of point() is in boost::optional<Point3>, as sometimes
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// the triangulation operation inside smart factor will encounter degeneracy.
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boost::optional<Point3> point = smart->point(result);
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if (point) // ignore if boost::optional return NULL
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landmark_result.insert(j, *point);
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}
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}
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landmark_result.print("Landmark results:\n");
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cout << "final error: " << graph.error(result) << endl;
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cout << "number of iterations: " << optimizer.iterations() << endl;
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return 0;
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}
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/* ************************************************************************* */
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