Sensor Planning for Mobile Robot Localization---A Hierarchical Approach Using a Bayesian Network and a Particle Filter

Authors:
Hongjun Zhou;S. Sakane
Affiliations:
Metropolitan Ind. Technol. Res. Inst., Tokyo;-
Venue:
IEEE Transactions on Robotics
Year:
2008

Citing 0
Cited 6

Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks

Information Sciences: an International Journal
An improvement on resampling algorithm of particle filters

IEEE Transactions on Signal Processing
Entropy-based active vision for a humanoid soccer robot

RoboCup 2010
Intrinsic Bayesian model for high-dimensional unsupervised reduction

Neurocomputing
Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters

Journal of Signal Processing Systems
Prognostics of Analog Filters Based on Particle Filters Using Frequency Features

Journal of Electronic Testing: Theory and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a hierarchical approach to solving sensor planning for the global localization of a mobile robot. Our system consists of two subsystems: a lower layer and a higher layer. The lower layer uses a particle filter to evaluate the posterior probability of the localization. When the particles converge into clusters, the higher layer starts particle clustering and sensor planning to generate an optimal sensing action sequence for the localization. The higher layer uses a Bayesian network for probabilistic inference. The sensor planning takes into account both localization belief and sensing cost. We conducted simulations and actual robot experiments to validate our proposed approach.