Abstract:
In this work, a vision-based solution is developed to address the challenge of landing on a ship deck with precision and accuracy. For an autonomous landing, it is important to have a fast and accurate pose estimation system along with a reliable control strategy. This research uses fractal ArUCo markers instead of multiple separate markers to allow smooth pose estimation at different heights. Pose estimates are further improved using an Extended Kalman Filter, and a tracking algorithm then uses these estimates to guide the landing. A four degree-of-freedom (roll, pitch, heave and sway) simulator platform was built and used to validate the algorithm. The accuracy of the vision system is compared against that of a motion capture system. Real-world experiments were performed on different quadrotors to demonstrate tracking and landing on the platform with sway, roll, and pitch motions. The results show that the system is efficient and reliable in achieving safe and successful landings. The proposed landing system is concluded to be applicable for landings on the deck of the ship under sea-state 4.