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

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 vision-sensor 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.