Intuitive 3D Maps for MAV Terrain Exploration and Obstacle Avoidance

Recent development showed that Micro Aerial Vehicles (MAVs) are nowadays capable of autonomously take off at one point and land at another using only one single camera as exteroceptive sensor. During the flight and landing phase the MAV and user have, however, little knowledge about the whole terrain and potential obstacles. In this paper we show a new solution for a real-time dense 3D terrain reconstruction. This can be used for efficient unmanned MAV terrain exploration and yields a solid base for standard autonomous obstacle avoidance algorithms and path planners. Our approach is based on a textured 3D mesh on sparse 3D point features of the scene. We use the same feature points to localize and control the vehicle in the 3D space as we do for building the 3D terrain reconstruction mesh. This enables us to reconstruct the terrain without significant additional cost and thus in real-time. Experiments show that the MAV is easily guided through an unknown, GPS denied environment. Obstacles are recognized in the iteratively built 3D terrain reconstruction and are thus well avoided.