Using Flow Field Divergence for Obstacle Avoidance:
Towards Qualitative Vision

Randal C. Nelson and John Aloimonos
Department of Computer Science
University of Rochester
Rochester, NY 14627

Abstract: The use of certain measures of flow field divergence is investigated as a qualitative cue for obstacle avoidance during visual navigation. It is shown that a quantity termed the directional divergence of the 2-D motion field can be used as a reliable indicator of the presence of obstacles in the visual field of an observer undergoing generalized rotational and translational motion. Moreover, the necessary measurements can be robustly obtained from real image sequences. A procedure for robustly extracting divergence information from image sequences which can be performed using a highly parallel architecture is described. Experimental results are presented showing that the system responds as expected to divergence in real world image sequences, and the use of the system to navigate between obstacles is demonstrated.