Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, due to the usage of the accurate sensors, distinct particles which compensate one another are attenuated as the RBPF-SLAM continues. To avoid this particle depletion, we propose the strategic games to assign the particle’s payoff which replaces the importance weight in the current RBPF-SLAM framework. From simulation works, we show that RBPF-SLAM with the strategic games is inconsistent in the pessimistic way, which is different from the existing optimistic RBPF-SLAM. In addition, first, the estimation errors with applying the strategic games are much less than those of the standard RBPF-SLAM, and second, the particle depletion is alleviated.

• 곽노산