Vision-Based Autonomous Ship Deck Landing of an Unmanned Aerial Vehicle Using Fractal ArUco Markers
Chiranjeev Prachand, Rahul Rustagi, Ritwik Shankar, Jitendra Singh, Abhishek Abhishek, K. Subramanian Venkatesh · 2025
Autonomous landing of an aircraft on a ship deck, perturbed by the winds and sea waves, is an inherently difficult task. To achieve this, we not only need to touchdown on a very small area, but we also need to time it accurately for a safe landing. In this paper, we explore a vision-based solution for precision landing using fractal ArUCo markers. We study the performance of these fractal markers in estimating the position with varying motions, especially with rolling and pitching motions. We then proceed to use this estimate in developing a landing algorithm that times the touchdown with respect to the rolling and pitching motion. To test the accuracy and efficiency of the above system, we built a low-cost 3 DoF ship deck emulator as a landing platform. The platform has increased roll, pitch, and heave motion anges compared to those readily available in the market. Finally, the results show that our landing system is capable of landing a UAV on a ship deck, which emulates the rolling and pitching motion of a ship in a wave condition up to sea state 4.