SLAM algorithms in MRPT
Not all SLAM algorithms fit any kind of observation (sensor data) and produce any map type. The following summarizes the SLAM algorithms implemented in MRPT and their associated map and observation types, grouped by input sensors.
- 2D laser scanner mrpt::obs::CObservation2DRangeScan:
To generate 2D occupancy grids (mrpt::maps::COccupancyGridMap2D) or point clouds (mrpt::maps::CPointsMap).
- Sonar sensors mrpt::obs::CObservationRange:
To generate 2D occupancy grids (mrpt::maps::COccupancyGridMap2D):
Range-bearing landmarks (mrpt::obs::CObservationBearingRange):
EKF-based SLAM: kf-slam
Monocular image features (visual keypoint matches from a visual SLAM front-end):
Back-end only: vision_bundle_adj_example
Range-only sensors (mrpt::obs::CObservationBeaconRanges):
Relative poses (Pose-graph or Graph-SLAM):
Graph-SLAM maps (Write me!) https://www.mrpt.org/Graph-SLAM_maps