To Smooth or to Filter: A Comparative Study of State Estimation Approaches for Vision-Based Autonomous Underwater Docking

Dörner, David; Espinoza, Aldo Terán; Torroba, Ignacio; Kuttenkeuler, Jakob; Stenius, Ivan

doi:10.1109/oceans51537.2024.10682396

OCEANS 2024 - Singapore 2024

DOI: 10.1109/oceans51537.2024.10682396

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To Smooth or to Filter: A Comparative Study of State Estimation Approaches for Vision-Based Autonomous Underwater Docking

David Dörner,

Aldo Terán Espinoza,

Ignacio Torroba

et al.

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2024

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Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon

Li,

Sun,

Han

et al. 2024

Drones

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Visual recognition and localization of underwater optical beacons are critical for AUV docking, but traditional beacons are limited by fixed directionality and light attenuation in water. To extend the range of optical docking, this study designs a novel omnidirectional rotating optical beacon that provides 360-degree light coverage over 45 m, improving beacon detection probability through synchronized scanning. Addressing the challenges of light centroid detection, we introduce a parallel deep learning detection algorithm based on an improved YOLOv8-pose model. Initially, an underwater optical beacon dataset encompassing various light patterns was constructed. Subsequently, the network was optimized by incorporating a small detection head, implementing dynamic convolution and receptive-field attention convolution for single-stage multi-scale localization. A post-processing method based on keypoint joint IoU matching was proposed to filter redundant detections. The algorithm achieved 93.9% AP at 36.5 FPS, with at least a 5.8% increase in detection accuracy over existing methods. Moreover, a light-source-based measurement method was developed to accurately detect the beacon’s orientation. Experimental results indicate that this scheme can achieve high-precision omnidirectional guidance with azimuth and pose estimation errors of -4.54° and 3.09°, respectively, providing a reliable solution for long-range and large-scale optical docking.

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Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon

Li,

Sun,

Han

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

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To Smooth or to Filter: A Comparative Study of State Estimation Approaches for Vision-Based Autonomous Underwater Docking

Cited by 1 publication

References 28 publications

Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon

Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon

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