Authors:
Y. Touati
1
;
Y. Amirat
1
;
Z. Djama
1
and
A. Ali Chérif
2
Affiliations:
1
LISSI Laboratory, Paris 12 University, France
;
2
LISSI Laboratory, Paris 12 University; University of Paris 8, France
Keyword(s):
Mobile robot, Robust Localization, Multiple Model, Hybrid Systems, Kalman Filtering, Data Fusion.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Robotics and Automation
;
Vehicle Control Applications
Abstract:
This paper focuses on robust pose estimation for mobile robot localization. The main idea of the approach proposed here is to consider the localization process as a hybrid process which evolves according to a model among a set of models with jumps between these models according to a Markov chain. In order to improve the robustness of the localization process, an on line adaptive estimation approach of noise statistics (state and observation), is applied for each mode. To demonstrate the validity of the proposed approach and to show its effectiveness, we’ve compared it to the standard approaches. For this purpose, simulations were carried out to analyze the performances of each approach in various scenarios.