Integration of a prediction mechanism with a sensor model: an anticipatory Bayes filter

Authors:
Guoxuan Zhang;Il Hong Suh
Affiliations:
College of Information and Communications, Hanyang University, Korea;College of Information and Communications, Hanyang University, Korea
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 6
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IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems

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Abstract

In the task of robot localization, Bayes filters use two processes: the prediction step and the measurement-update step. Briefly, the state transition model is responsible for prediction, and the sensor model is responsible for measurement updates. This paper presents a new approach to the sensor model, called the predictive sensor model, which utilizes a prediction mechanism to improve the efficiency of measurement updates in Bayes filters. By adding sensorial anticipation, we extend the original Bayes filter to an anticipatory Bayes filter. We also propose an entropy-based place-segmentation method for automatic segmentation of sequentially collected visionsensor data. Our place segmentation technique is most useful for node clustering in the process of constructing topological maps. Our work was verified by experiments using observed data.