A visionbased system for autonomous vertical landing of unmanned aerial vehicles Over the last few years, different researchers have been developing protocols and applications in order to land unmanned aerial vehicles (UAVs) autonomously. However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications, such as package retrieval and delivery. Therefore, in this paper, we present a solution for high precision landing based on the use of ArUco markers. In our solution, a UAV equipped with a camera is able to detect ArUco markers from an altitude of 20 meters. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. We evaluated our proposal using our own UAV simulation platform (ArduSim), and validated it using real UAVs. The results show an average offset of only 11 centimeters, which vastly improves the landing accuracy compared to the traditional GPSbased landing, that typically deviates from the intended target by 1 to 3 meters.

Visionbased Autonomous Landing for Micro Aerial Vehicles on Targets Moving in 3D Space A strategy for autonomous landing of Micro Aerial Vehicles (MAVs) on moving platforms is presented, based only on visual information from a monocular camera. The landing target is uniquely identified by previously known Augmented Reality (AR) markers, and its relative pose is estimated by visual servoing algorithms. Target trajectory in  mathbbR3  is composed of planar translation and vertical oscillation, simulating a vessel that travels in foul weather. The visual feedback helps the aerial robot to track this vessel, while a trajectory planning method, based on the systemu0027s model, allows predicting its future pose. Simulated results using the ROS framework are used to verify the effectiveness of our proposed method.