Distributed unscented marginalized particle filter for simultaneous localization and mapping - 文章详情页

作者：

Pei, Fu-Jun (Pei, Fu-Jun.) | Li, Hao-Yang (Li, Hao-Yang.) | Wu, Mei (Wu, Mei.)

收录：

EI Scopus PKU CSCD

摘要：

Aimed　at　the　problems　of　low　precision,　large　amount　of　calculation　and　severe　sample　degeneracy　of　simultaneous　localization　and　mapping(SLAM),　this　paper　presented　a　distributed　unscented　marginalized　particle　filter(DUMPF)　algorithm　based　on　the　combination　of　the　distributed　unscented　particle　filter(DUPF)　with　the　marginalized　particle　filter(MPF).　In　the　proposed　method,　the　SLAM　system　was　divided　into　several　subsystems　according　to　the　distribution　algorithms.　The　unscented　particle　filter(UPF)　was　used　in　each　subsystem　to　estimate　a　part　of　the　states.　The　marginal　distribution　of　the　UPF　was　optimized　to　reduce　the　computational　complexity.　The　estimated　results　of　the　subsystems　were　transmitted　to　the　master　filter　to　obtain　the　final　result.　The　simulation　results　showed　that　the　improved　DUMPF　could　prevent　the　particle　degeneration　problem,　and　had　a　higher　precision　and　a　smaller　computational　complexity.

关键词：

Computational complexity Mapping Monte Carlo methods Robotics

作者机构：

[ 1 ] [Pei, Fu-Jun]Department of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Li, Hao-Yang]Department of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Wu, Mei]Department of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China

通讯作者信息：

[pei, fu-jun]department of electronic information and control engineering, beijing university of technology, beijing; 100124, china

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