Robust light source detection for AUV docking

Abstract: For Autonomous Underwater Vehicles (AUVs) to be able to conduct longterm surveys, the ability to return to a docking station for maintenance and recharging is crucial. A dynamic docking system where a slowly moving submarine acts as the docking station provides increased hydrodynamic control and reduces the impact of environmental disturbances. A vision-based relative positioning system using a camera, mounted on the AUV, and light sources, mounted on the docking station, is investigated as a suitable high-resolution and high-frequency solution for a short-range relative positioning system. Detection and identification of the true light sources in the presence of reflections, ambient light, and other luminaries, requires a robust tracking pipeline that can reject false positives. In this thesis, we present a complete tracking pipeline, from image processing to pose estimation, specifically for a soft docking scenario. We highlight the issues of light source detectors based on finding a unique global threshold and detectors based on gradient information and propose a novel method, based on using a suitable threshold for each light source. Rejection of false positives is handled systematically by rejecting pose estimates resulting in large re-projection errors, and a configuration of the light sources is proposed that enhances the pose estimation performance. The performance of the proposed light source detector is evaluated on the D-recovery dataset. Results show that the proposed method outperforms other methods in identifying the light sources. The tracking pipeline is evaluated with experiments as well as a simulation based on the Stonefish simulator.