Random set approach to distributed multivehicle SLAM

This paper deals with the simultaneous localization and mapping (SLAM) problem for autonomous vehicles or mobile robots. More specifically, a multi-vehicle scenario is considered wherein a team of vehicles explore the scene of interest in order to cooperatively construct the map of the environment by locally updating and exchanging map information in a neighbor-wise fashion. A random-set approach is undertaken by regarding the map as a random finite set (RFS) and updating the first-order moment, called probability hypothesis density (PHD), of its multi-object density. Consensus on PHDs is adopted in order to spread the map information through the team of vehicles also taking into account the different and time-varying fields of view (FoVs) of the team members. The developed algorithm represents - to the best of the authors’ knowledge - the first attempt to solve in a fully decentralized way the multi-vehicle SLAM problem within the RFS framework. The effectiveness of the proposed approach is tested by means of simulation experiments.