Map merging of Multi-Robot SLAM using Reinforcement Learning

Abstract

Using `Simultaneous Localization and Mapping' (SLAM), mobile robots can become truly autonomous in the exploration of their environment. However, once these environments becomes too large, Multi-Robot SLAM becomes a requirement. This paper will outline how a mobile robot should decide when best to merge its maps with another robot's upon rendezvous, as opposed to doing so immediately. This decision will be based on the current status of the mapping particle filters and the current status of the environment. Using Reinforcement Learning, a model can be established and then trained upon to determine a policy capable of deciding when best to merge. This will allow the robot to incur less error during a merge compared to simply merging immediately. This policy is trained and validated using simulated mobile robot datasets.