UAV MISSION PLANNING FOR AUTOMATIC EXPLORATION AND SEMANTIC MAPPING
Unmanned Aerial Vehicles (UAVs) are used for the inspection of areas which are otherwise difficult to access. Autonomous monitoring and navigation requires a background knowledge on the surroundings of the vehicle. Most mission planing systems assume collision-free pre-defined paths and do not tolerate a GPS signal outage. Our approach makes weaker assumptions. This paper introduces a mission planing platform allowing for the integration of environmental prior knowledge such as 3D building and terrain models. This prior knowledge is integrated to pre-compute an octomap for collision detection. The semantically rich building models are used to specify semantic user queries such as roof or facade inspection. A reasoning process paves the way for semantic mission planing of hidden and a-priori unknown objects. Subsequent scene interpretation is performed by an incremental parsing process.