Bundle-Adjustment methods

// global functions

double mrpt::vision::bundle_adj_full(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    mrpt::vision::TFramePosesVec& frame_poses,
    mrpt::vision::TLandmarkLocationsVec& landmark_points,
    const mrpt::containers::yaml& extra_params = {},
    const mrpt::vision::TBundleAdjustmentFeedbackFunctor user_feedback = mrpt::vision::TBundleAdjustmentFeedbackFunctor()
    );

void mrpt::vision::ba_initial_estimate(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    mrpt::vision::TFramePosesVec& frame_poses,
    mrpt::vision::TLandmarkLocationsVec& landmark_points
    );

void mrpt::vision::ba_initial_estimate(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    mrpt::vision::TFramePosesMap& frame_poses,
    mrpt::vision::TLandmarkLocationsMap& landmark_points
    );

double mrpt::vision::reprojectionResiduals(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    const mrpt::vision::TFramePosesVec& frame_poses,
    const mrpt::vision::TLandmarkLocationsVec& landmark_points,
    std::vector<std::array<double, 2>>& out_residuals,
    const bool frame_poses_are_inverse,
    const bool use_robust_kernel = true,
    const double kernel_param = 3.0,
    std::vector<double>* out_kernel_1st_deriv = nullptr
    );

double mrpt::vision::reprojectionResiduals(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    const mrpt::vision::TFramePosesMap& frame_poses,
    const mrpt::vision::TLandmarkLocationsMap& landmark_points,
    std::vector<std::array<double, 2>>& out_residuals,
    const bool frame_poses_are_inverse,
    const bool use_robust_kernel = true,
    const double kernel_param = 3.0,
    std::vector<double>* out_kernel_1st_deriv = nullptr
    );

void mrpt::vision::add_se3_deltas_to_frames(
    const mrpt::vision::TFramePosesVec& frame_poses,
    const mrpt::math::CVectorDouble& delta,
    const size_t delta_first_idx,
    const size_t delta_num_vals,
    mrpt::vision::TFramePosesVec& new_frame_poses,
    const size_t num_fix_frames
    );

void mrpt::vision::add_3d_deltas_to_points(
    const mrpt::vision::TLandmarkLocationsVec& landmark_points,
    const mrpt::math::CVectorDouble& delta,
    const size_t delta_first_idx,
    const size_t delta_num_vals,
    mrpt::vision::TLandmarkLocationsVec& new_landmark_points,
    const size_t num_fix_points
    );

Global Functions

double mrpt::vision::bundle_adj_full(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    mrpt::vision::TFramePosesVec& frame_poses,
    mrpt::vision::TLandmarkLocationsVec& landmark_points,
    const mrpt::containers::yaml& extra_params = {},
    const mrpt::vision::TBundleAdjustmentFeedbackFunctor user_feedback = mrpt::vision::TBundleAdjustmentFeedbackFunctor()
    )

Sparse Levenberg-Marquart solution to bundle adjustment - optimizes all the camera frames & the landmark locations.

At input a gross estimation of the frame poses & the landmark points must be supplied. If you don’t have such a starting point, use mrpt::vision::ba_initial_estimate() to compute it.

At output the best found solution will be returned in the variables. Optionally, a functor can be passed for having feedback on the progress at each iteration (you can use it to refresh a GUI, display a progress bar, etc…).

This implementation is almost entirely an adapted version from RobotVision (at OpenSLAM.org) (C) by Hauke Strasdat (Imperial College London), licensed under GNU LGPL. See the related paper: H. Strasdat, J.M.M. Montiel, A.J. Davison: “Scale Drift-Aware Large Scale Monocular SLAM”, RSS2010, http://www.roboticsproceedings.org/rss06/p10.html

List of optional parameters in “extra_params”:

  • “verbose” : Verbose output (default=0)

  • “max_iterations”: Maximum number of iterations to run (default=50)

  • “robust_kernel”: If !=0, use a robust kernel against outliers (default=1)

  • “kernel_param”: The pseudo-huber kernel parameter (default=3)

  • “mu”: Initial mu for LevMarq (default=-1 -> autoguess)

  • “num_fix_frames”: Number of first frame poses to don’t optimize (keep unmodified as they come in) (default=1: the first pose is the reference and is not modified)

  • “num_fix_points”: Idem, for the landmarks positions (default=0: optimize all)

  • “profiler”: If !=0, displays profiling information to the console at return.

In this function, all coordinates are absolute. Camera frames are such that +Z points forward from the focal point (see the figure in mrpt::obs::CObservationImage).

The first frame pose will be not updated since at least one frame must remain fixed.

Parameters:

observations

[IN] All the feature observations (WITHOUT distortion), indexed by feature ID as lists of <frame_ID, (x,y)>. See TSequenceFeatureObservations.

camera_params

[IN] The camera parameters, mainly used for the intrinsic 3x3 matrix. Distortion params are ignored since it’s assumed that observations are already undistorted pixels.

frame_poses

[IN/OUT] Input: Gross estimation of each frame poses. Output: The found optimal solution.

landmark_points

[IN/OUT] Input: Gross estimation of each landmark point. Output: The found optimal solution.

extra_params

[IN] Optional extra parameters. Read above.

user_feedback

[IN] If provided, this functor will be called at each iteration to provide a feedback to the user.

Returns:

The final overall squared error.

void mrpt::vision::ba_initial_estimate(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    mrpt::vision::TFramePosesVec& frame_poses,
    mrpt::vision::TLandmarkLocationsVec& landmark_points
    )

Fills the frames & landmark points maps with an initial gross estimate from the sequence observations, so they can be fed to bundle adjustment methods.

See also:

bundle_adj_full

void mrpt::vision::ba_initial_estimate(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    mrpt::vision::TFramePosesMap& frame_poses,
    mrpt::vision::TLandmarkLocationsMap& landmark_points
    )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

double mrpt::vision::reprojectionResiduals(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    const mrpt::vision::TFramePosesVec& frame_poses,
    const mrpt::vision::TLandmarkLocationsVec& landmark_points,
    std::vector<std::array<double, 2>>& out_residuals,
    const bool frame_poses_are_inverse,
    const bool use_robust_kernel = true,
    const double kernel_param = 3.0,
    std::vector<double>* out_kernel_1st_deriv = nullptr
    )

Compute reprojection error vector (used from within Bundle Adjustment methods, but can be used in general) See mrpt::vision::bundle_adj_full for a description of most parameters.

Parameters:

frame_poses_are_inverse

If set to true, global camera poses are \(\ominus F\) instead of \(F\), for each F in frame_poses.

Returns:

Overall squared reprojection error.

double mrpt::vision::reprojectionResiduals(
    const mrpt::vision::TSequenceFeatureObservations& observations,
    const mrpt::img::TCamera& camera_params,
    const mrpt::vision::TFramePosesMap& frame_poses,
    const mrpt::vision::TLandmarkLocationsMap& landmark_points,
    std::vector<std::array<double, 2>>& out_residuals,
    const bool frame_poses_are_inverse,
    const bool use_robust_kernel = true,
    const double kernel_param = 3.0,
    std::vector<double>* out_kernel_1st_deriv = nullptr
    )

Compute reprojection error vector (used from within Bundle Adjustment methods, but can be used in general) See mrpt::vision::bundle_adj_full for a description of most parameters.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Returns:

Overall squared reprojection error.

void mrpt::vision::add_se3_deltas_to_frames(
    const mrpt::vision::TFramePosesVec& frame_poses,
    const mrpt::math::CVectorDouble& delta,
    const size_t delta_first_idx,
    const size_t delta_num_vals,
    mrpt::vision::TFramePosesVec& new_frame_poses,
    const size_t num_fix_frames
    )

For each pose in the vector frame_poses, adds a “delta” increment to the manifold, with the “delta” given in the se(3) Lie algebra:

new_frame_poses[i] = frame_poses[i] [+] delta[(first_idx+6*i):(first_idx+6*i+5)] , for every pose in frame_poses

With the left-multiplication convention of the manifold exp(delta) operator, that is:

p <-- p [+] delta ==>  p <-- exp(delta) * p

Parameters:

delta_num_vals

Used just for sanity check, must be equal to “frame_poses.size() * 6”

 
void mrpt::vision::add_3d_deltas_to_points(
    const mrpt::vision::TLandmarkLocationsVec& landmark_points,
    const mrpt::math::CVectorDouble& delta,
    const size_t delta_first_idx,
    const size_t delta_num_vals,
    mrpt::vision::TLandmarkLocationsVec& new_landmark_points,
    const size_t num_fix_points
    )

For each pose in the vector frame_poses, adds a “delta” increment to the manifold, with the “delta” given in the se(3) Lie algebra:

new_landmark_points[i] = landmark_points[i] + delta[(first_idx+3*i):(first_idx+3*i+2)] , for every pose in landmark_points

Parameters:

delta_num_vals

Used just for sanity check, must be equal to “landmark_points.size() * 3”