ABSTRACT
Vision-based mobile robot navigation requires robust methods for planning and executing tasks due to the unreliability of visual information. In this paper we propose a new method for reliable vision-based naviga tion in an unmodeled dynamic environment. Artificial landmarks are used as visual cues for navigation. Our system builds a visibility graph among landmark loca tions dunng an exploration phase and then uses that graph for navigation. To deal mth temporary occlusion of landmarks, long-term changes in the environment, and inherent uncertainties in the landmark detection process, we use a probabilistic model of landmark vis ibility. Based on the history of previous observations made, each visibility edge in the graph is annotated with an estimated probability of landmark detection. To solve a navigation task, our algorithm computes the expected shortest paths between all landmarks and the specified goal, by solving a special instance of a Markov decision process. The paper presents both the proba bilistic expected shortest path planner and the landmark design and detection algorithm, which finds landmark patterns under general affine transformations in real time.
