Title :
An experimental comparison of localization methods continued
Author :
Gutmann, Jens-Steffen ; Fox, Dieter
Author_Institution :
Digital Creatures Lab., Sony Corp., Tokyo, Japan
Abstract :
Localization is one of the fundamental problems in mobile robot navigation. Past experiments have shown that, in general, grid-based Markov localization is more robust than Kalman filtering while the latter can be more accurate than the former Recently new methods for localization employing particle filters have become popular. In this paper, we compare different localization methods using Kalman filtering, grid-based Markov localization, Monte Carlo Localization (MCL), and combinations thereof. We give experimental evidence that a combination of Markov localization and Kalman filtering as well as a variant of MCL outperform the other methods in terms of accuracy, robustness, and time needed for recovering from manual robot displacement, while requiring only few computational resources.
Keywords :
Kalman filters; Monte Carlo methods; computerised navigation; mobile robots; position control; Kalman filtering; Monte Carlo localization; accuracy; grid-based Markov localization; localization methods comparison; manual robot displacement; mobile robot navigation; particle filters; recovery time; robustness; Adaptive filters; Filtering; Kalman filters; Legged locomotion; Mobile robots; Monte Carlo methods; Navigation; Performance evaluation; Robot sensing systems; Robustness;
Conference_Titel :
Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7398-7
DOI :
10.1109/IRDS.2002.1041432