Abstract-We present a quadrotor system capable of autonomously landing on a moving platform using only onboard sensing and computing. We rely on state-of-the-art computer vision algorithms, multi-sensor fusion for localization of the robot, detection and motion estimation of the moving platform, and path planning for fully autonomous navigation. Our system does not require any external infrastructure, such as motioncapture systems. No prior information about the location of the moving landing target is needed. We validate our system in both synthetic and real-world experiments using low-cost and lightweight consumer hardware. To the best of our knowledge, this is the first demonstration of a fully autonomous quadrotor system capable of landing on a moving target, using only onboard sensing and computing, without relying on any external infrastructure. SUPPLEMENTARY MATERIALVideo of the experiments: https://youtu.be/ Tz5ubwoAfNE

Autonomous Drone Racing (ADR) is a challenge for autonomous drones to navigate a cluttered indoor environment without relying on any external sensing in which all the sensing and computing must be done on board. Although no team could complete the whole racing track so far, most successful teams implemented waypoint tracking methods and robust visual recognition of the gates of distinct colors because the complete environmental information was given to participants before the events. In this paper, we introduce the purpose of ADR as a benchmark testing ground for autonomous drone technologies and analyze the challenges and technologies used in the two previous ADRs held in IROS 2016 and IROS 2017. Six teams that participated in these events present their implemented technologies that cover modifyed ORBSLAM, robust alignment method for waypoints deployment, sensor fusion for motion estimation, deep learning for gate detection and motion control, and stereo-vision for gate detection.