int main(int argc, char** argv){
typedef PinholePose<Cal3_S2> Camera;
typedef SmartProjectionPoseFactor<Cal3_S2> SmartFactor;
Values initial_estimate;
NonlinearFactorGraph graph;
const noiseModel::Isotropic::shared_ptr model = noiseModel::Isotropic::Sigma(2,1);
string calibration_loc = findExampleDataFile("VO_calibration.txt");
string pose_loc = findExampleDataFile("VO_camera_poses_large.txt");
string factor_loc = findExampleDataFile("VO_stereo_factors_large.txt");
//read camera calibration info from file
// focal lengths fx, fy, skew s, principal point u0, v0, baseline b
cout << "Reading calibration info" << endl;
ifstream calibration_file(calibration_loc.c_str());
double fx, fy, s, u0, v0, b;
calibration_file >> fx >> fy >> s >> u0 >> v0 >> b;
const Cal3_S2::shared_ptr K(new Cal3_S2(fx, fy, s, u0, v0));
cout << "Reading camera poses" << endl;
ifstream pose_file(pose_loc.c_str());
int pose_index;
MatrixRowMajor m(4,4);
//read camera pose parameters and use to make initial estimates of camera poses
while (pose_file >> pose_index) {
for (int i = 0; i < 16; i++)
pose_file >> m.data()[i];
initial_estimate.insert(pose_index, Pose3(m));
}
// landmark keys
size_t landmark_key;
// pixel coordinates uL, uR, v (same for left/right images due to rectification)
// landmark coordinates X, Y, Z in camera frame, resulting from triangulation
double uL, uR, v, X, Y, Z;
ifstream factor_file(factor_loc.c_str());
cout << "Reading stereo factors" << endl;
//read stereo measurements and construct smart factors
SmartFactor::shared_ptr factor(new SmartFactor(model, K));
size_t current_l = 3; // hardcoded landmark ID from first measurement
while (factor_file >> pose_index >> landmark_key >> uL >> uR >> v >> X >> Y >> Z) {
if(current_l != landmark_key) {
graph.push_back(factor);
factor = SmartFactor::shared_ptr(new SmartFactor(model, K));
current_l = landmark_key;
}
factor->add(Point2(uL,v), pose_index);
}
Pose3 firstPose = initial_estimate.at<Pose3>(1);
//constrain the first pose such that it cannot change from its original value during optimization
// NOTE: NonlinearEquality forces the optimizer to use QR rather than Cholesky
// QR is much slower than Cholesky, but numerically more stable
graph.push_back(NonlinearEquality<Pose3>(1,firstPose));
LevenbergMarquardtParams params;
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
params.verbosity = NonlinearOptimizerParams::ERROR;
cout << "Optimizing" << endl;
//create Levenberg-Marquardt optimizer to optimize the factor graph
LevenbergMarquardtOptimizer optimizer = LevenbergMarquardtOptimizer(graph, initial_estimate, params);
Values result = optimizer.optimize();
cout << "Final result sample:" << endl;
Values pose_values = result.filter<Pose3>();
pose_values.print("Final camera poses:\n");
return 0;
}
int main(int argc, char** argv){
Values initial_estimate;
NonlinearFactorGraph graph;
const noiseModel::Isotropic::shared_ptr model = noiseModel::Isotropic::Sigma(3,1);
string calibration_loc = findExampleDataFile("VO_calibration.txt");
string pose_loc = findExampleDataFile("VO_camera_poses_large.txt");
string factor_loc = findExampleDataFile("VO_stereo_factors_large.txt");
//read camera calibration info from file
// focal lengths fx, fy, skew s, principal point u0, v0, baseline b
double fx, fy, s, u0, v0, b;
ifstream calibration_file(calibration_loc.c_str());
cout << "Reading calibration info" << endl;
calibration_file >> fx >> fy >> s >> u0 >> v0 >> b;
//create stereo camera calibration object
const Cal3_S2Stereo::shared_ptr K(new Cal3_S2Stereo(fx,fy,s,u0,v0,b));
ifstream pose_file(pose_loc.c_str());
cout << "Reading camera poses" << endl;
int pose_id;
MatrixRowMajor m(4,4);
//read camera pose parameters and use to make initial estimates of camera poses
while (pose_file >> pose_id) {
for (int i = 0; i < 16; i++) {
pose_file >> m.data()[i];
}
initial_estimate.insert(Symbol('x', pose_id), Pose3(m));
}
// camera and landmark keys
size_t x, l;
// pixel coordinates uL, uR, v (same for left/right images due to rectification)
// landmark coordinates X, Y, Z in camera frame, resulting from triangulation
double uL, uR, v, X, Y, Z;
ifstream factor_file(factor_loc.c_str());
cout << "Reading stereo factors" << endl;
//read stereo measurement details from file and use to create and add GenericStereoFactor objects to the graph representation
while (factor_file >> x >> l >> uL >> uR >> v >> X >> Y >> Z) {
graph.push_back(
GenericStereoFactor<Pose3, Point3>(StereoPoint2(uL, uR, v), model,
Symbol('x', x), Symbol('l', l), K));
//if the landmark variable included in this factor has not yet been added to the initial variable value estimate, add it
if (!initial_estimate.exists(Symbol('l', l))) {
Pose3 camPose = initial_estimate.at<Pose3>(Symbol('x', x));
//transform_from() transforms the input Point3 from the camera pose space, camPose, to the global space
Point3 worldPoint = camPose.transform_from(Point3(X, Y, Z));
initial_estimate.insert(Symbol('l', l), worldPoint);
}
}
Pose3 first_pose = initial_estimate.at<Pose3>(Symbol('x',1));
//constrain the first pose such that it cannot change from its original value during optimization
// NOTE: NonlinearEquality forces the optimizer to use QR rather than Cholesky
// QR is much slower than Cholesky, but numerically more stable
graph.push_back(NonlinearEquality<Pose3>(Symbol('x',1),first_pose));
cout << "Optimizing" << endl;
//create Levenberg-Marquardt optimizer to optimize the factor graph
LevenbergMarquardtOptimizer optimizer = LevenbergMarquardtOptimizer(graph, initial_estimate);
Values result = optimizer.optimize();
cout << "Final result sample:" << endl;
Values pose_values = result.filter<Pose3>();
pose_values.print("Final camera poses:\n");
return 0;
}