SmartFactors¶
These are “structure-less” factors, i.e., rather than introducing a new variable for an observed 3D point or landmark, a single factor is created that provides a multi-view constraint on several poses and/or cameras.
SmartRangeFactor¶
An experiment in creating a structure-less 2D range-SLAM factor with range-only measurements.
It uses a sophisticated triangulate logic based on circle intersections.
SmartStereoProjectionFactor¶
Version of SmartProjectionFactor for stereo observations, specializes SmartFactorBase for CAMERA == StereoCamera.
TODO: a lot of commented out code and could move a lot to .cpp file.
SmartStereoProjectionPoseFactor¶
Derives from SmartStereoProjectionFactor but adds an array of Cal3_S2Stereo calibration objects .
TODO: Again, as no template arguments, we could move a lot to .cpp file.
SmartStereoProjectionFactorPP¶
Similar SmartStereoProjectionPoseFactor but additionally adds an array of body_P_cam poses. The dimensions seem to be hardcoded and the types defined in the SmartFactorBase have been re-defined.
The body_P_cam poses are optimized here!
TODO: See above, same issues as SmartStereoProjectionPoseFactor.
SmartProjectionPoseFactorRollingShutter¶
Is templated on a CAMERA type and derives from SmartProjectionFactor.
This factor optimizes two consecutive poses of a body assuming a rolling shutter model of the camera with given readout time. The factor requires that values contain (for each 2D observation) two consecutive camera poses from which the 2D observation pose can be interpolated.
TODO: the dimensions seem to be hardcoded and the types defined in the SmartFactorBase have been re-defined. Also, possibly a lot of copy/paste computation of things that (should) happen in base class.
KnownLandmarkSquaredRangeFactor¶
KnownLandmarkSquaredRangeFactor models squared ranges to fixed known landmarks from Euclidean point states. It supports ordinary nonlinear optimization and QCQP conversion through the unstable AugmentedPoint<N> type. Both types and their typed Values/QCQP helpers are exposed in Python by gtsam_unstable; MATLAB uses gtsam.KnownLandmarkSquaredRangeFactor* through the unstable toolbox.
See the 3D localization example for nonlinear, QCQP, and optional SDP solutions.
The MATLAB regression tests are in matlab/gtsam_unstable_tests/; add that directory to the MATLAB path and run testAugmentedPoint and testKnownLandmarkSquaredRangeFactor after building both toolboxes.