Unmanned Aerial Vehicles (UAVs) autonomous approaching and landing on mobile platforms always play an important role in various application scenarios. Such a complicated autonomous task requires an integrated multi-sensor system to guarantee environmental adaptability in contrast to using each sensor individually. Multi-sensor fusion perception demonstrates great feasibility to compensate for adverse visual events, undesired vibrations of inertia sensors, and satellite positioning loss. In this paper, a UAV autonomous landing scheme based on multi-sensor fusion is proposed. In particular, Ultra Wide-Band (UWB) sensor, Inertial Measurement Unit (IMU), and vision feedback are integrated to guide the UAV to approach and land on a moving object. In the approaching stage, a UWB-IMU-based sensor fusion algorithm is proposed to provide relative position estimation of vehicles with real time and high consistency. Such a sensor integration addresses the open challenge of inaccurate satellite positioning when the UAV is near the ground. It can also be extended to satellite-denied environmental applications. When the landing platform is detected by the onboard camera, the UAV performs autonomous landing. In the landing stage, the vision sensor is involved. With the visual feedback, a deep-learning-based detector and local pose estimator are enabled when the UAV approaches the landing platform. To validate the feasibility of the proposed autonomous landing scheme, both simulation and real-world experiments in extensive scenes are performed. As a result, the proposed landing scheme can land successfully with adequate accuracy in most common scenarios.

For unmanned aerial vehicles (UAVs), landing on a moving vehicle robustly is an open challenge, especially under cluttered surroundings with the presence of unknown obstacles. Those undesired environmental factors could induce collisions and thus affect flight safety significantly. Currently, there are few solutions to address such a challenge. In this paper, we propose a systematic autonomous landing scheme that enables the robust autonomous landing performance of a quadrotor UAV. The proposed scheme integrates target detection, state estimation, trajectory planning, and landing control. The position and attitude information of the target ground vehicle and the test quadrotor are estimated by the onboard vision system and GPS. In order to detect landing markers at different altitudes, a particular landing pad with an Apriltag bundle is implemented. As a typical aerial–terrestrial cooperation system, the trajectory planner of the quadrotor updates continuously to avoid obstacles via real-time sensing and re-planning. A finite state machine is used to label the current flight status and triggers the control laws correspondingly. The effectiveness of the proposed method has been validated in a high-fidelity simulator with environmental obstacles.