Expansion resetting for recovery from fatal error in Monte Carlo localization - comparison with sensor resetting methods

Author

Ueda, Ryuichi ; Arai, Tamio ; Sakamoto, Kohei ; Kikuchi, Toshifumi ; Kamiya, Shogo

Author_Institution

Dept. of Precision Eng., Tokyo Univ., Japan

Volume

3

fYear

2004

fDate

28 Sept.-2 Oct. 2004

Firstpage

2481

Abstract

Though Monte Carlo localization is a popular method for mobile robot localization, it requires a method for recovery of large estimation error in itself. In this paper, a recovery method, which is named an expansion resetting method, is newly proposed. A blending of the expansion resetting method and another, which is called the sensor resetting method, is also proposed. We then compared our methods and others in a simulated RoboCup environment. Typical accidents for mobile robots were produced in the simulator during trials. We could grasp the characteristics of each method. Especially, the blending method was robust against the kidnapped robot problems.

Keywords

Monte Carlo methods; mobile robots; path planning; Monte Carlo localization; blending method; mobile robot localization; sensor resetting method; Accidents; Estimation error; Filters; Mathematical model; Mobile robots; Monte Carlo methods; Precision engineering; Probability distribution; Robot sensing systems; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on

Print_ISBN

0-7803-8463-6

Type

conf

DOI

10.1109/IROS.2004.1389781

Filename

1389781