Stereo vision-based obstacle avoidance for micro air vehicles using disparity space

We address obstacle avoidance for outdoor flight of micro air vehicles. The highly textured nature of outdoor scenes enables camera-based perception, which will scale to very small size, weight, and power with very wide, two-axis field of regard. In this paper, we use forward-looking stereo cameras for obstacle detection and a downward-looking camera as an input to state estimation. For obstacle representation, we use image space with the stereo disparity map itself. We show that a C-space-like obstacle expansion can be done with this representation and that collision checking can be done by projecting candidate 3-D trajectories into image space and performing a z-buffer-like operation with the disparity map. This approach is very efficient in memory and computing time. We do motion planning and trajectory generation with an adaptation of a closed-loop RRT planner to quadrotor dynamics and full 3D search. We validate the performance of the system with Monte Carlo simulations in virtual worlds and flight tests of a real quadrotor through a grove of trees. The approach is designed to support scalability to high speed flight and has numerous possible generalizations to use other polar or hybrid polar/Cartesian representations and to fuse data from additional sensors, such as peripheral optical flow or radar.