Entropy-based gaze planning

This paper describes an algorithm for recognizing known objects in an unstructured environment (e.g. landmarks) from measurements acquired with a single monochrome television camera mounted on a mobile observer. The approach is based on the concept of an entropy map, which is used to guide the mobile observer along an optimal trajectory that minimizes the ambiguity of recognition as well as the amount of data that must be gathered. Recognition itself is based on the optical flow signatures that result from the camera motion signatures that are inherently ambiguous due to the confounding of motion, structure and imaging parameters. We show how gaze planning partially alleviates this problem by generating trajectories that maximize discriminability. A sequential Bayes approach is used to handle the remaining ambiguity by accumulating evidence for different object hypotheses over time until a clear assertion can be made. Results from an experimental recognition system using a gantry-mounted television camera are presented to show the effectiveness of the algorithm on a large class of common objects.