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ExpressionExample now only uses BADFactors and yields same result as SFMExample

release/4.3a0
dellaert 2014-10-02 11:21:24 +02:00
parent ebb091d390
commit bef23a2008
2 changed files with 19 additions and 14 deletions
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2
examples/SFMExample.cpp
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@ -111,6 +111,8 @@ int main(int argc, char* argv[]) {
/* Optimize the graph and print results */
Values result = DoglegOptimizer(graph, initialEstimate).optimize();
result.print("Final results:\n");
cout << "initial error = " << graph.error(initialEstimate) << endl;
cout << "final error = " << graph.error(result) << endl;
return 0;
}

31
gtsam_unstable/examples/ExpressionExample.cpp
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@ -44,7 +44,7 @@ using namespace gtsam;
/* ************************************************************************* */
int main(int argc, char* argv[]) {
Cal3_S2::shared_ptr K(new Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
Cal3_S2 K(50.0, 50.0, 0.0, 50.0, 50.0);
noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
// Create the set of ground-truth landmarks and poses
@ -60,37 +60,40 @@ int main(int argc, char* argv[]) {
// Here we don't use a PriorFactor but directly the BADFactor class
// The object x0 is an Expression, and we create a factor wanting it to be equal to poses[0]
Pose3_ x0('x',0);
// graph.push_back(BADFactor<Pose3>(poses[0], x0, poseNoise));
graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise)); // add directly to graph
graph.push_back(BADFactor<Pose3>(poseNoise, poses[0], x0));
// We create a constant Expression for the calibration here
Cal3_S2_ cK(K);
// Simulated measurements from each camera pose, adding them to the factor graph
for (size_t i = 0; i < poses.size(); ++i) {
for (size_t j = 0; j < points.size(); ++j) {
SimpleCamera camera(poses[i], *K);
SimpleCamera camera(poses[i], K);
Point2 measurement = camera.project(points[j]);
graph.push_back(GenericProjectionFactor<Pose3, Point3, Cal3_S2>(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K));
// Below an expression for the prediction of the measurement:
Pose3_ x('x', i);
Point3_ p('l', j);
Expression<Point2> prediction = uncalibrate(cK, project(transform_to(x, p)));
// Again, here we use a BADFactor
graph.push_back(BADFactor<Point2>(measurementNoise, measurement, prediction));
}
}
// Because the structure-from-motion problem has a scale ambiguity, the problem is still under-constrained
// Here we add a prior on the position of the first landmark. This fixes the scale by indicating the distance
// between the first camera and the first landmark. All other landmark positions are interpreted using this scale.
// Add prior on first point to constrain scale, again with BADFActor
noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
graph.push_back(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
graph.print("Factor Graph:\n");
graph.push_back(BADFactor<Point3>(pointNoise, points[0], Point3_('l', 0)));
// Create the data structure to hold the initial estimate to the solution
// Intentionally initialize the variables off from the ground truth
// Create perturbed initial
Values initialEstimate;
for (size_t i = 0; i < poses.size(); ++i)
initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
for (size_t j = 0; j < points.size(); ++j)
initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
initialEstimate.print("Initial Estimates:\n");
cout << "initial error = " << graph.error(initialEstimate) << endl;
/* Optimize the graph and print results */
Values result = DoglegOptimizer(graph, initialEstimate).optimize();
result.print("Final results:\n");
cout << "final error = " << graph.error(result) << endl;
return 0;
}