Vision-based Three Dimensional Navigation Frame Mapping and Planning for Collision Avoidance for Micro Air Vehicles

Abstract

Path planning and obstacle avoidance involve planning a feasible path from the initial to goal conflgurations while avoiding obstacles. This paper presents a vision-based three dimensional navigation frame mapping and path planning technique for collision avoidance for Micro Air Vehicles (MAVs) using a forward-looking onboard camera. Using computer vision algorithms, a depth map representing the range and bearing to obstacles is obtained. Based on the depth map, we use the extended Kalman Filter (EKF) to estimate the range and azimuth to obstacles and the height of obstacles and use the joint compatibility branch and bound (JCBB) algorithm to associate the camera measurements with the existing obstacles. A three dimensional map, constructed in cylindrical coordinates, is then created in the navigation frame of the MAV. We employ the Rapidly-Exploring Random Tree (RRT) algorithm to flnd collision-free Dubins paths in the navigation frame. The simulation results show that the proposed technique is successful in solving path planning and multiple obstacles avoidance problems for MAVs.