A Multisession SLAM Approach for RatSLAM

To successfully perform autonomous navigation, mobile agents must solve the Simultaneous Localization and Mapping (SLAM) problem. However, acquiring the map in a single SLAM session may not be possible, thus the map may be incrementally built over multiple sessions. Two solutions could be considered to solve the multisession SLAM problem: (i) the robot must localize itself in the previously stored map before the new session starts; (ii) it can start a new map and merge it with the map from the previous sessions. To date, only scenario (i) has been addressed by RatSLAM, an algorithm inspired by the navigation system in rodent brains. Therefore, this work proposes a multisession solution that solves both scenarios. A new mechanism merges the data from the RatSLAM structures of the current mapping session with those previously stored if there are connections between these paths. This approach was tested in four different scenarios, from virtual controlled environments to real-world environments with two, three, and five sessions. The robot started in an unfamiliar location for each mapping session, but it also works if the agent starts in a known place, scenario (ii) and (i), respectively. For all experiments, the entire map was consistently obtained. Furthermore, the proposed approach updates and enhances the previous session's map in real-world environments. Therefore, the proposed approach may be a multiple SLAM session solution for the RatSLAM algorithm.

Automated summary

To successfully perform autonomous navigation, mobile agents must solve the Simultaneous Localization and Mapping (SLAM) problem. This work proposes a multisession solution that solves both scenarios in the multisession SLAM problem. The proposed approach merges data from the current mapping session with those previously stored if there are connections between these paths. This approach was tested in different scenarios and consistently obtained the entire map.