Vision-based local multi-resolution mapping and path planning for Miniature Air Vehicles

Abstract

Miniature Air Vehicles (MAVs) are often used for low altitude flights where unknown obstacles might be encountered. Path planning and obstacle avoidance for MAVs involve planning a feasible path from an initial state to a goal state while avoiding obstacles in the environment. This paper presents a vision-based local multi-resolution mapping and path planning technique for MAVs using a forward-looking onboard camera. A depth map, which represents the time-to-collision (TTC) and bearing information of the obstacles, is obtained by computer vision algorithms. To account for measurement uncertainties introduced by the camera, a multi-resolution map in the body frame of the MAV is created in polar coordinates. Using the depth map, the locations of the obstacles are determined in the multi-resolution map. Dijkstra's algorithm is employed to find a collision-free path in the body frame. The simulation and flight test results show that the proposed technique is successful in solving path planning and multiple obstacles avoidance problems for MAVs.