| Περίληψη: | The use of unmanned aerial vehicles (UAVs) is increasing across many modern civil applica-tion domains, including wireless coverage, delivery, precision agriculture, search and rescue.UAVs equipped with bio-radars, camera sensors and inertial measurement unit (IMU) are life-saving tech-nology that can empower the identification of survivors in catastrophe scenarios and provide medicalaid. However, these UAVs need to be capable of autonomously landing on complex terrains. This isextremely challenging as the structure of these terrains is often unknown, and no prior knowledge canbe leveraged. In this thesis, we present a vision-based autonomous landing system for UAV equippedwith stereo cameras and IMU. The landing site detection algorithm considers several hazardous fac-tors including flatness, steepness and depth accuracy, to compute a weighted cost-map based on whichwe detect dense candidate landing sites. The current pose of theunmanned aerial vehicle (UAV) is es-timated by fusing raw data from the inertial sensors with the pose obtained from stereo ORB-SLAM2.
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