Real time biologically-inspired depth maps from spherical flow

We present a strategy for generating real-time relative depth maps of an environment from optical flow, under general motion. We achieve this using an insect-inspired hemispherical fish-eye sensor with 190 degree FOV, and a derotated optical flow field. The de-rotation algorithm applied is based on the theoretical work of Nelson and Aloimonos [10]. From this we obtain the translational component of motion, and construct full relative depth maps on the sphere. We examine the robustness of this strategy in both simulation and realworld experiments, for a variety of environmental scenarios. To our knowledge, this is the first demonstrated implementation of the Nelson and Aloimonos algorithm working in real-time, over real image sequences. In addition, we apply this algorithm to the real-time recovery of full relative depth maps. These preliminary results demonstrate the feasibility of this approach for closed-loop control of a robot.