Abstract— This paper addresses a multi-robot SLAM approach based on the Kalman consensus filter (KCF). Under the unknown initial condition, a reference robot designates the initial poses of other robots when the first rendezvous between them occurs. Accordingly, past and current poses and maps of these robots are estimated by an acausal filter and a causal filter. If initialized robots meet again, their current poses are updated using the KCF. Accordingly, their past poses and maps until the most recent rendezvous are also compensated through the acausal filter. In two simulations, the FastSLAM algorithm, which is a special case of Rao-Blackwellized particle filters, was employed for SLAM. The performance of the proposed approach was verified by comparing conventional approaches.

Index Terms— Kalman consensus filter, Rao-Blackwellized particle filter, Multi-robot SLAM, FastSLAM

Cite: Seung-Hwan Lee and Beom H. Lee, "Kalman Consensus Based Multi-Robot SLAM with a Rao-Blackwellized Particle Filter," Jounal of Automation and Control Engineering, Vol. 3, No. 5, pp. 368-372, October, 2015. doi: 10.12720/joace.3.5.368-372