Probabilistic multi-component extended strong tracking filter for mobile robot global localization

Authors:
Zhibin Liu;Zongying Shi;Wenli Xu
Affiliations:
Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, P.R.China;Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, P.R.China;Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, P.R.China
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 7
Cited 0

Robust Monte Carlo localization for mobile robots

Artificial Intelligence
Robust global localization using clustered particle filtering

Eighteenth national conference on Artificial intelligence
A relative map approach to SLAM based on shift and rotation invariants

Robotics and Autonomous Systems
Adaptive Dynamic Clustered Particle Filtering for Mobile Robots Global Localization

Journal of Intelligent and Robotic Systems
Estimating the absolute position of a mobile robot using position probability grids

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Omnidirectional vision scan matching for robot localization in dynamic environments

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a multi-component extended strong tracking filter (MESTer) for global localization. It is the first time strong tracking filter (STF) is introduced into robotics domain and is fundamentally extended to be suitable for fusing observations with arbitrary time-varying dimensionality, based on equivalent space transformation and extended orthogonality principle. The resulted extended strong tracking filter (ESTF) is then combined with a probabilistic multi-component evolving mechanism and finally forms the MESTer localization method. Real robot experiments and comparisons with existing methods show that MESTer has high convergence speed, computational efficiency and definite robustness to sensor noises, kidnapped robot problem, system nonlinearities, and symmetric environments.