Autonomous landing on pipes using soft gripper for inspection and maintenance in outdoor environments The use of unmanned aerial systems for industrial applications has evolved considerably in recent years. This paper presents an aerial system capable of perching autonomously on pipes for inspection and maintenance in industrial environments. The target pipe to perch on is detected using a visual algorithm based on a semantic convolutional neuronal network. The information from a color camera is used to segment the image. Then, the segmentation information is fused with a depth image to estimate the pipexe2x80x99s pose, so that the pose of the robot can be controlled relative to it. The aerial robot is equipped with a soft landing system that robustly attaches it to the pipe. The article presents the complete development of the system. Experimental results performed in outdoor environments are shown.

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.