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New example now uses EssentialTransferFactor
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dellaert committed Oct 26, 2024
1 parent 7f49d06 commit 5893b17
Showing 1 changed file with 42 additions and 40 deletions.
82 changes: 42 additions & 40 deletions examples/EssentialViewGraphExample.cpp
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*/

#include <gtsam/geometry/Cal3_S2.h>
#include <gtsam/geometry/EssentialMatrix.h>
#include <gtsam/geometry/PinholeCamera.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Point3.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/inference/EdgeKey.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/sfm/TransferFactor.h>
#include <gtsam/sfm/TransferFactor.h> // Contains EssentialTransferFactor

#include <vector>

#include "SFMdata.h"
#include "gtsam/inference/Key.h"
#include "SFMdata.h" // For createPoints() and posesOnCircle()

using namespace std;
using namespace gtsam;
using namespace symbol_shorthand; // For K(symbol)

/* ************************************************************************* */
// Main function
int main(int argc, char* argv[]) {
// Define the camera calibration parameters
Cal3_S2 K(50.0, 50.0, 0.0, 50.0, 50.0);
Cal3_S2 K_initial(50.0, 50.0, 0.0, 50.0, 50.0);

// Create the set of 8 ground-truth landmarks
vector<Point3> points = createPoints();

// Create the set of 4 ground-truth poses
vector<Pose3> poses = posesOnCircle(4, 30);

// Calculate ground truth fundamental matrices, 1 and 2 poses apart
auto F1 = FundamentalMatrix(K, poses[0].between(poses[1]), K);
auto F2 = FundamentalMatrix(K, poses[0].between(poses[2]), K);
// Calculate ground truth essential matrices, 1 and 2 poses apart
auto E1 = EssentialMatrix::FromPose3(poses[0].between(poses[1]));
auto E2 = EssentialMatrix::FromPose3(poses[0].between(poses[2]));

// Simulate measurements from each camera pose
std::array<std::array<Point2, 8>, 4> p;
for (size_t i = 0; i < 4; ++i) {
PinholeCamera<Cal3_S2> camera(poses[i], K);
PinholeCamera<Cal3_S2> camera(poses[i], K_initial);
for (size_t j = 0; j < 8; ++j) {
p[i][j] = camera.project(points[j]);
}
}

// This section of the code is inspired by the work of Sweeney et al.
// [link](sites.cs.ucsb.edu/~holl/pubs/Sweeney-2015-ICCV.pdf) on view-graph
// calibration. The graph is made up of transfer factors that enforce the
// epipolar constraint between corresponding points across three views, as
// described in the paper. Rather than adding one ternary error term per point
// in a triplet, we add three binary factors for sparsity during optimization.
// In this version, we only include triplets between 3 successive cameras.
// Create the factor graph
NonlinearFactorGraph graph;
using Factor = TransferFactor<FundamentalMatrix>;
using Factor = EssentialTransferFactor<Cal3_S2>;

for (size_t a = 0; a < 4; ++a) {
size_t b = (a + 1) % 4; // Next camera
Expand All @@ -83,54 +79,60 @@ int main(int argc, char* argv[]) {
tuples3.emplace_back(p[c][j], p[b][j], p[a][j]);
}

// Add transfer factors between views a, b, and c. Note that the EdgeKeys
// are crucial in performing the transfer in the right direction. We use
// exactly 8 unique EdgeKeys, corresponding to 8 unknown fundamental
// matrices we will optimize for.
// Add transfer factors between views a, b, and c.
graph.emplace_shared<Factor>(EdgeKey(a, c), EdgeKey(b, c), tuples1);
graph.emplace_shared<Factor>(EdgeKey(a, b), EdgeKey(b, c), tuples2);
graph.emplace_shared<Factor>(EdgeKey(a, c), EdgeKey(a, b), tuples3);
}

// Formatter for printing keys
auto formatter = [](Key key) {
EdgeKey edge(key);
return (std::string)edge;
if (Symbol(key).chr() == 'k') {
return (string)Symbol(key);
} else {
EdgeKey edge(key);
return (std::string)edge;
}
};

graph.print("Factor Graph:\n", formatter);

// Create a delta vector to perturb the ground truth
// We can't really go far before convergence becomes problematic :-(
Vector7 delta;
delta << 1, 2, 3, 4, 5, 6, 7;
delta *= 1e-5;
// Create a delta vector to perturb the ground truth (small perturbation)
Vector5 delta;
delta << 1, 1, 1, 1, 1;
delta *= 1e-2;

// Create the data structure to hold the initial estimate to the solution
// Create the initial estimate for essential matrices
Values initialEstimate;
for (size_t a = 0; a < 4; ++a) {
size_t b = (a + 1) % 4; // Next camera
size_t c = (a + 2) % 4; // Camera after next
initialEstimate.insert(EdgeKey(a, b), F1.retract(delta));
initialEstimate.insert(EdgeKey(a, c), F2.retract(delta));
initialEstimate.insert(EdgeKey(a, b), E1.retract(delta));
initialEstimate.insert(EdgeKey(a, c), E2.retract(delta));
}

// Insert initial calibrations (using K symbol)
for (size_t i = 0; i < 4; ++i) {
initialEstimate.insert(K(i), K_initial);
}

initialEstimate.print("Initial Estimates:\n", formatter);
graph.printErrors(initialEstimate, "errors: ", formatter);
graph.printErrors(initialEstimate, "Initial Errors:\n", formatter);

/* Optimize the graph and print results */
// Optimize the graph and print results
LevenbergMarquardtParams params;
params.setlambdaInitial(1000.0); // Initialize lambda to a high value
params.setVerbosityLM("SUMMARY");
Values result =
LevenbergMarquardtOptimizer(graph, initialEstimate, params).optimize();

cout << "initial error = " << graph.error(initialEstimate) << endl;
cout << "final error = " << graph.error(result) << endl;
cout << "Initial error = " << graph.error(initialEstimate) << endl;
cout << "Final error = " << graph.error(result) << endl;

result.print("Final results:\n", formatter);
result.print("Final Results:\n", formatter);

cout << "Ground Truth F1:\n" << F1.matrix() << endl;
cout << "Ground Truth F2:\n" << F2.matrix() << endl;
cout << "Ground Truth E1:\n" << E1.matrix() << endl;
cout << "Ground Truth E2:\n" << E2.matrix() << endl;

return 0;
}
/* ************************************************************************* */
}

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