Improvement for the rao-blackwellized particle filters SLAM with MCMC resampling - Details

Author：

Wang, Huan (Wang, Huan.) | Liu, Hongyun (Liu, Hongyun.) | Ju, Hehua (Ju, Hehua.) | Li, Xiuzhi (Li, Xiuzhi.)

Indexed by：

EI Scopus

Abstract：

The　ability　to　simultaneously　locate　a　robot　and　accurately　map　its　surroundings　is　considered　to　be　a　key　prerequisite　of　truly　autonomous　robots.　Rao-Blackwellized　particle　filters　simultaneous　localization　and　mapping　can　produce　accurate　results　but　it　has　the　tendency　to　become　overconfident.　In　this　paper,　the　analysis　on　consistency　is　presented.　The　methodology　of　the　Markov　Chain　Monte　Carlo　resampling　is　incorporated　to　prevent　particle　impoverishment.　The　algorithms　are　evaluated　on　accuracy　and　consistency　using　computer　simulation.　Experimental　results　show　that　the　increased　diversity　of　particles　can　improve　the　accuracy　as　well　as　consistency　of　RBPF　SLAM.　©2009　IEEE.

Keyword：

Robotics Software engineering Mapping Markov processes Monte Carlo methods

Author Community：

[ 1 ] [Wang, Huan]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Liu, Hongyun]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
[ 3 ] [Ju, Hehua]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
[ 4 ] [Li, Xiuzhi]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China