I would like your advice regarding tunning the ‘MemoryThr’, in order to utilize the long-term memory mechanism. There is clearly an accuracy-performance tradeoff with this parameter, as when I keep all the nodes in the working memory, I get the best accuracy, but the performance decreases due to the large map size in memory.
Could you please suggest a way to set ‘MemoryThr’ value based on the planned trajectory size of the expected mapping session, or based on the known warehouse size?
In general I prefer to set "Rtabmap/TimeThr` to adjust depending on the computation power available (e.g., setting Rtabmap/TimeThr=0.7 when Rtabmap/DetectionRate=1), though "Rtabmap/MemoryThr" can give more similar results on different computers (as it is independent of the computation power). Both parameters will trigger memory management approach of RTAB-Map. When using memory management, we should use RGBD/OptimizeFromGraphEnd to true to avoid the robot to jump, this makes the map always transformed in odom frame (/map->/odom will be always identity). The parameter "Mem/RehearsalSimilarity" should also be carefully tuned, not too high or too low otherwise always old nodes from WM will be transferred to LTM first, removing the ability to find loop closures on old nodes. In general, to tune it I would do a mapping session and check "Memory -> Rehearsal sim" statistics:
In this case, I would set the parameter to "Mem/RehearsalSimilarity=0.2" so that the weight of the nodes (over that threshold) are uniformly distributed in the environment while not adding too many of them.
For path planning to an area that is transferred in LTM, you would have to call "/rtabmap/set_goal" service to a known node id or label:
General thoughts: Note that if you are planning to map the whole warehouse, then use the map in localization mode, and if you don't care to actually see in real-time the loop closure detected while mapping, you could disable loop closure detection (Kp/MaxFeatures=-1) and just make rtabmap accumulate the nodes in the database. Offline, use `rtabmap-reprocess` while re-enabling loop closure detection (adding option "--Kp/MaxFeatures 500") to remap without memory management. In localization mode, rtabmap should be able handle online very large maps without the need of memory management. The memory management approach is mostly useful when we want to do continuous SLAM.