TABLE OF CONTENTS Amy J. Briggs, Middlebury College, Middlebury, VT
Daniel Scharstein, Middlebury College, Middlebury, VT
Stephen D. Abbott, Middlebury College, Middlebury, VT
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 navigation in an unmodeled dynamic environment. Artificial landmarks are used as visual cues for navigation. Our system builds a visibility graph among landmark locations during an exploration phase and then uses that graph for navigation. To deal with 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 visibility. 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 probabilistic expected shortest path planner and the landmark design and detection algorithm, which finds landmark patterns under general affine transformations in real-time.
Vision-based mobile robot navigation is often planned using landmarks, either artificial or extracted from the environment. Such landmarks must be detected quickly and as reliably as possible. When landmark detection is unreliable due to factors such as temporary occlusion or varying lighting conditions, the planner should compute motion paths dynamically...
