Distributed and decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks

Abstract

This paper presents a simultaneous localization and mapping (SLAM) algorithm that allows a recursive state estimation process to be both distributed and decentralized in a sparse robot network that is never guaranteed to be fully connected (communication-wise). In such a sparse network, a robot may not always have the latest odometry and measurements from other robots. Our approach allows robots to obtain a temporary (localization and map) estimate at the current timestep using information available locally, but we also ensure that the centralized-equivalent estimate can always be recovered by all robots at a later time; we do not require a robot to keep track of what other robots know when it applies the Markov property to discard past information. Our method is validated through a hardware SLAM experiment where we distribute data association hypotheses amongst a team of robots. Estimate errors are shown to validate the performance of our approach. We also discuss the trade-offs and show comparisons between our distributed approach versus a non-distributed one.