137 lines
5.0 KiB
C++
137 lines
5.0 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 ViewGraphExample.cpp
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* @brief View-graph calibration on a simulated dataset, a la Sweeney 2015
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* @author Frank Dellaert
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* @date October 2024
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*/
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#include <gtsam/geometry/Cal3_S2.h>
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#include <gtsam/geometry/PinholeCamera.h>
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/inference/EdgeKey.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/sfm/TransferFactor.h>
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#include <vector>
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#include "SFMdata.h"
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#include "gtsam/inference/Key.h"
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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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// Define the camera calibration parameters
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Cal3_S2 cal(50.0, 50.0, 0.0, 50.0, 50.0);
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// Create the set of 8 ground-truth landmarks
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vector<Point3> points = createPoints();
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// Create the set of 4 ground-truth poses
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vector<Pose3> poses = posesOnCircle(4, 30);
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// Calculate ground truth fundamental matrices, 1 and 2 poses apart
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auto F1 = FundamentalMatrix(cal.K(), poses[0].between(poses[1]), cal.K());
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auto F2 = FundamentalMatrix(cal.K(), poses[0].between(poses[2]), cal.K());
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// Simulate measurements from each camera pose
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std::array<std::array<Point2, 8>, 4> p;
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for (size_t i = 0; i < 4; ++i) {
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PinholeCamera<Cal3_S2> camera(poses[i], cal);
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for (size_t j = 0; j < 8; ++j) {
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p[i][j] = camera.project(points[j]);
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}
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}
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// This section of the code is inspired by the work of Sweeney et al.
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// [link](sites.cs.ucsb.edu/~holl/pubs/Sweeney-2015-ICCV.pdf) on view-graph
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// calibration. The graph is made up of transfer factors that enforce the
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// epipolar constraint between corresponding points across three views, as
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// described in the paper. Rather than adding one ternary error term per point
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// in a triplet, we add three binary factors for sparsity during optimization.
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// In this version, we only include triplets between 3 successive cameras.
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NonlinearFactorGraph graph;
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using Factor = TransferFactor<FundamentalMatrix>;
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for (size_t a = 0; a < 4; ++a) {
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size_t b = (a + 1) % 4; // Next camera
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size_t c = (a + 2) % 4; // Camera after next
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// Vectors to collect tuples for each factor
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std::vector<std::tuple<Point2, Point2, Point2>> tuples1, tuples2, tuples3;
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// Collect data for the three factors
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for (size_t j = 0; j < 8; ++j) {
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tuples1.emplace_back(p[a][j], p[b][j], p[c][j]);
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tuples2.emplace_back(p[a][j], p[c][j], p[b][j]);
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tuples3.emplace_back(p[c][j], p[b][j], p[a][j]);
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}
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// Add transfer factors between views a, b, and c. Note that the EdgeKeys
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// are crucial in performing the transfer in the right direction. We use
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// exactly 8 unique EdgeKeys, corresponding to 8 unknown fundamental
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// matrices we will optimize for.
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graph.emplace_shared<Factor>(EdgeKey(a, c), EdgeKey(b, c), tuples1);
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graph.emplace_shared<Factor>(EdgeKey(a, b), EdgeKey(b, c), tuples2);
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graph.emplace_shared<Factor>(EdgeKey(a, c), EdgeKey(a, b), tuples3);
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}
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auto formatter = [](Key key) {
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EdgeKey edge(key);
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return (std::string)edge;
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};
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graph.print("Factor Graph:\n", formatter);
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// Create a delta vector to perturb the ground truth
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// We can't really go far before convergence becomes problematic :-(
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Vector7 delta;
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delta << 1, 2, 3, 4, 5, 6, 7;
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delta *= 1e-5;
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// Create the data structure to hold the initial estimate to the solution
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Values initialEstimate;
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for (size_t a = 0; a < 4; ++a) {
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size_t b = (a + 1) % 4; // Next camera
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size_t c = (a + 2) % 4; // Camera after next
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initialEstimate.insert(EdgeKey(a, b), F1.retract(delta));
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initialEstimate.insert(EdgeKey(a, c), F2.retract(delta));
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}
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initialEstimate.print("Initial Estimates:\n", formatter);
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graph.printErrors(initialEstimate, "errors: ", formatter);
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/* Optimize the graph and print results */
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LevenbergMarquardtParams params;
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params.setlambdaInitial(1000.0); // Initialize lambda to a high value
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params.setVerbosityLM("SUMMARY");
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Values result =
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LevenbergMarquardtOptimizer(graph, initialEstimate, params).optimize();
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cout << "initial error = " << graph.error(initialEstimate) << endl;
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cout << "final error = " << graph.error(result) << endl;
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result.print("Final results:\n", formatter);
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cout << "Ground Truth F1:\n" << F1.matrix() << endl;
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cout << "Ground Truth F2:\n" << F2.matrix() << endl;
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return 0;
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}
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/* ************************************************************************* */
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