An Improved Rao-Blackwellized Particle Filter for SLAM

Author

Haijun Wang ; Shaoliang Wei ; Yimin Chen

Author_Institution

Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai

fYear

2008

fDate

21-22 Dec. 2008

Firstpage

515

Lastpage

518

Abstract

Simultaneous localization and map building (SLAM) is one of the fundamental problems in robot navigation, and FastSLAM algorithms based on Rao-Blackwellized particle filters (RBPF) have become popular tools to solve the SLAM problems. For solving the potential limitations, which are the derivation of the Jacobian matrices, and particles impoverishment in SLAM algorithms, this paper proposes an improved algorithm based on unscented Kalman filter (UKF) for landmark feature estimate and particles resampling strategy to overcome the above- mentioned drawbacks. Experimental results demonstrate the effectiveness of the proposed algorithm.

Keywords

Kalman filters; SLAM (robots); mobile robots; particle filtering (numerical methods); path planning; FastSLAM algorithms; Jacobian matrices; Rao-Blackwellized particle filter; SLAM; particles resampling strategy; simultaneous localization and map building; unscented Kalman filter; Application software; Educational institutions; Information filters; Information technology; Intelligent structures; Jacobian matrices; Particle filters; Robots; Simultaneous localization and mapping; State estimation; Nonlinear state estimate; Rao-Blackwellized Particle filter; Topological map; Unscented Kalman Filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application Workshops, 2008. IITAW '08. International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3505-0

Type

conf

DOI

10.1109/IITA.Workshops.2008.150

Filename

4731990