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Unmanned aerial vehicles are becoming an important part of the modern life. Despite some recent advances in GPS-aided navigation of quadrotors, the concern of crash and collision still overshadows their reliability and safety, especially in GPS-denied environments. Therefore, the necessity for developing fully automatic methods for safe, accurate, and independent landing of drones increases over time. This paper investigates the autolanding process by focusing on an accurate and continuous position estimation of the drone using a monocular vision system and the fusion with the inertial measurement unit and ultrasonic sensors' data. An ARUCO marker is used as the landing pad, and the information is processed in the ground station through a real-time Wi-Fi link. In order to overcome the closed loop instability caused by the communication and localization delays, we propose a method called "movement slicing method". This method divides the moves around the marker into moving and waiting slices and makes the landing process not only more accurate but also faster. Experimental results show a successful landing of the UAV on a predefined location, while it is accurately aligned with the marker using the proposed method.

Automatic landing of a low-cost quadrotor using monocular vision and Kalmanfilter in GPS-denied environments