1A1-E18 Dispersion Technique for Particle Diversity in FastSLAM

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概要

• 論文の詳細を見る

• FastSLAM is a widely-used framework for simultaneous localization and mapping using a Rao-Blackwellized particle filter. However, FastSLAM degenerates over time due to the particle depletion in resampling phase, mainly caused by the loss of particle diversity. In this work, a dispersion technique for recovering the particle diversity is proposed to improve the performance of FastSLAM. First, when particle diversity is lost after resampling phase, a source which helps particles be dispersed is generated from the positions and weights of particles. Then, the particles are dispersed statistically and geometrically using their own weights and the source. The performance of the proposed technique was verified by computer simulations, which showed smaller RMS errors in both robot and feature positions than conventional FastSLAM.

• 一般社団法人日本機械学会の論文
• 2009-05-25

著者

• Lee Beom

  Asri Department Of Electrical Engineering Seoul National University

• LEE Beom

  ASRI, Department of Electrical Engineering, Seoul National University

• LEE Tae

  ASRI, Department of Electrical Engineering, Seoul National University

• Lee Kong

  ASRI, Department of Electrical Engineering, Seoul National University

• Lee Heon

  ASRI, Department of Electrical Engineering, Seoul National University

• Lee Tae

  Asri Department Of Electrical Engineering Seoul National University

• Lee Kong

  Asri Department Of Electrical Engineering Seoul National University

• Lee Heon

  Asri Department Of Electrical Engineering Seoul National University

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• 2A1-F03 Improved 3-D Underwater Terrain Covering for Multi-AUV
• 2A1-F02 3D Collision-free Motion Planning for Multi-AUV
• 1A1-E18 Dispersion Technique for Particle Diversity in FastSLAM

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