1A1-E17 Strategic Games for Survival of Particles in Rao-Blackwellized Particle Filtering for SLAM

スポンサーリンク

概要

• 論文の詳細を見る

• 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.

• 一般社団法人日本機械学会の論文
• 2009-05-25

著者

• Kita Nobuyuki

  Intelligent Systems Division Electrotechnical Laboratory

• Kita Nobuyuki

  Intelligent Systems Research Institute National Institute Of Advanced Industrial Science And Technol

• KWAK Nosan

  Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Techno

• YOKOI Kazuhito

  Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Techno

• Yokoi Kazuhito

  Intelligent Systems Research Institute National Institute Of Advanced Industrial Science And Technol

• Kwak Nosan

  Intelligent Systems Research Institute National Institute Of Advanced Industrial Science And Technol

関連論文

• Mobile sensing robots for nuclear power plant inspection
• Active visual localization for multiple inspection robots
• 1A1-E17 Strategic Games for Survival of Particles in Rao-Blackwellized Particle Filtering for SLAM

スポンサーリンク