Vision-Based Obstacle Avoidance for Micro Air Vehicles Using an Egocylindrical Depth Map

Abstract

Obstacle avoidance is an essential capability for micro air vehicles. Prior approaches have mainly been either purely reactive, mapping low-level visual features directly to headings, or deliberative methods that use onboard 3-D sensors to create a 3-D, voxel-based world model, then generate 3-D trajectories and check them for potential collisions with the world model. Onboard 3-D sensor suites have had limited fields of view. We use forward-looking stereo vision and lateral structure from motion to give a very wide horizontal and vertical field of regard. We fuse depth maps from these sources in a novel robot-centered, cylindrical, inverse range map we call an egocylinder. Configuration space expansion directly on the egocylinder gives a very compact representation of visible freespace. This supports very efficient motion planning and collision-checking with better performance guarantees than standard reactive methods. We show the feasibility of this approach experimentally in a challenging outdoor environment.