Bayesian Robot Localization with Action-Associated Sparse Appearance-Based Map in a Dynamic Indoor Environment

This work considers robot localization with an action-associated sparse appearance-based map, under conditions with dynamic change in the environment. In this case, two significant problems must be solved for robust localization. The first involves variations in the environment caused by dynamic objects and changes in illumination, and the second arises from the nature of sparse appearance-based map. That is, a robot must be able to recognize observations taken at slightly different positions and angles within a certain region as identical. In this paper, we address a possible solution to these problems on the basis of a probabilistic model called the Bayes filter. Here, we propose an observation model based LeTO(2) function and an action-associated sparse appearance-based map to be used for prediction, update, and final localization steps. In addition, multiple visual features are used to increase the reliability of the observation model. We performed experiments to demonstrate the validity of the proposed approach under various conditions with regard to dynamic objects, illumination, and viewpoint. The results clearly demonstrated the value of our approach.