An integrative framework for global self-localization

Author

Weber, Joachirn ; Franken, Lutz ; Jörg, Klaus-Werner ; Schmitt, Klaus ; Von Puttkamer, Ewald

Author_Institution

Dept. of Comput. Sci., Kaiserslautern Univ., Germany

fYear

2001

fDate

2001

Firstpage

73

Lastpage

78

Abstract

Concerning the robustness of mobile robot navigation, global self-localization is a key feature for many service applications. In this paper we describe an efficient Bayesian approach for hybrid topological/metric navigation, which is designed to exploit information from multiple sources of sensor data. Experiments with a combination of odometry/laserscans/computer vision show the system was able to generate initial position hypotheses, to cope with environmental ambiguities and to recover from severe position errors.

Keywords

Bayes methods; computer vision; inference mechanisms; laser beam applications; mobile robots; navigation; path planning; sensor fusion; topology; Bayes rule; computer vision; laser scans; metric navigation; mobile robots; odometry; probabilistic reasoning; self localization; sensor fusion; topological navigation; Application software; Computer science; Mobile robots; Navigation; Process control; Robust control; Robustness; Sensor fusion; Sensor phenomena and characterization; Service robots;

fLanguage

English

Publisher

ieee

Conference_Titel

Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on

Print_ISBN

3-00-008260-3

Type

conf

DOI

10.1109/MFI.2001.1013511

Filename

1013511