An evaluation of the sequential Monte Carlo technique for simultaneous localisation and map-building

Author

Yuen, David C K ; MacDonald, Bruce A.

Author_Institution

Dept. of Electr. & Electron. Eng., Auckland Univ., New Zealand

Volume

2

fYear

2003

fDate

14-19 Sept. 2003

Firstpage

1564

Abstract

Simultaneous localisation and map-building (SLAM) can be considered as a combined state and parameter estimation problem. Instead of using extended Kalman filtering, a more flexible Sequential Monte Carlo method is considered. Multiple generic particle filters are initialised to estimate the robot and obstacle positions concurrently. Simulation results based on a simple robot environment, which represents obstacles by line segments, indicate the feasibility of the proposed method.

Keywords

Kalman filters; Monte Carlo methods; collision avoidance; filtering theory; mobile robots; parameter estimation; path planning; state estimation; extended Kalman filtering; line segments; map building; multiple generic particle filters; obstacle position; parameter estimation; robot environment; robot position; sequential Monte Carlo method; simultaneous localisation; state estimation; Filtering algorithms; Kalman filters; Monte Carlo methods; Particle filters; Robot sensing systems; Simultaneous localization and mapping; Sliding mode control; State estimation; Steady-state; Stereo vision;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on

ISSN

1050-4729

Print_ISBN

0-7803-7736-2

Type

conf

DOI

10.1109/ROBOT.2003.1241817

Filename

1241817