Move experimental methods to another branch, and update documentation.

mola_relocalization/CMakeLists.txt

@@ -31,7 +31,6 @@ mola_add_library(
  TARGET ${PROJECT_NAME}
  SOURCES 
  src/find_best_poses_se2.cpp
- src/AlignBEV.cpp
  src/RelocalizationICP_SE2.cpp
  src/RelocalizationLikelihood_SE2.cpp
  include/mola_relocalization/relocalization.h

mola_relocalization/README.md

@@ -3,7 +3,29 @@ C++ library with algorithms for relocalization, global localization, or pose est
 
 Note that particle filtering is implemented in its own repository under [mrpt_navigation](https://github.com/mrpt-ros-pkg/mrpt_navigation).
 
-Example result for the `mola::Relocalization_SE2` method (from a unit test, see [the code](https://github.com/MOLAorg/mola/blob/develop/mola_relocalization/tests/test-relocalization-se2-kitti.cpp) for details):
+## Method #1: mola::RelocalizationICP_SE2
+
+Takes a global and a local metric map, a SE(2) ROI, and tries to match
+the local map in the global map by running ICP from all initial guesses
+defined by a regular SE(2) lattice, returning the result as a SE(3) hashed
+lattice.
+
+This method is based on [mp2p_icp ICP pipelines](https://docs.mola-slam.org/latest/module-mp2p-icp.html).
+
+## Method #2: mola::RelocalizationLikelihood_SE2
+
+Takes a global metric map, an observation, and a SE(2) ROI, and evaluates
+the likelihood of the observation in a regular SE(2) lattice.
+
+This is based on mrpt maps observationLikelihood() evaluation, so the main
+parameters that determine the way likelihood is computed must be defined
+on before hand within each of the metric map layers of the input reference
+map.
+At present this algorithm is useful for these sensor/map types:
+- observations: pointclouds/2d_scan, reference_map: 2D gridmap
+- observations: pointclouds, reference_map: pointclouds
+
+Example result for the `mola::RelocalizationLikelihood_SE2` method (from a unit test, see [the code](https://github.com/MOLAorg/mola/blob/develop/mola_relocalization/tests/test-relocalization-se2-kitti.cpp) for details):
 
 ![mola_relocalize_figs](https://github.com/MOLAorg/mola/assets/5497818/6622739f-95ca-4e39-a770-d5f15c01adb3)
 

mola_relocalization/include/mola_relocalization/relocalization.h

@@ -147,37 +147,6 @@ struct RelocalizationICP_SE2
  static Output run(const Input& in);
 };
 
-/** Takes a global and a local point cloud maps, and builds a point cloud
- * density "grid map image" from a bird-eye-view (BEV), then applies the
- * grid-map align algorithm in mrpt::maps.
- *
- * \ingroup mola_relocalization_grp
- */
-struct AlignBEV
-{
- struct Input
- {
- mp2p_icp::metric_map_t reference_map;
- std::vector<std::string> ref_map_layers; //!< empty=all
- mp2p_icp::metric_map_t local_map;
- std::vector<std::string> local_map_layers; //!< empty=all
-
- double resolution_xy = 0.40;
-
- Input() = default;
- };
-
- struct Output
- {
- mola::HashedSetSE3 found_poses;
- double time_cost = .0; //!< [s]
-
- Output() = default;
- };
-
- static Output run(const Input& in);
-};
-
 /** Finds the SE(2) poses with the top given percentile likelihood, and returns
  * them sorted by likelihood (higher values are better matches).
  *