A depth-sensor-free next-best-view framework for efficient ArUco marker recognition in autonomous underwater vehicle charging operation

Wang, Yujia, Yu, Aidi, Fan, Yumeng, Gao, Yan and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2026. A depth-sensor-free next-best-view framework for efficient ArUco marker recognition in autonomous underwater vehicle charging operation. IEEE Internet of Things Journal 10.1109/jiot.2026.3728270

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Abstract

The sustainability of the Internet of Underwater Things (IoUT) relies on the continuous operation of Autonomous Underwater Vehicles (AUVs), where ArUco markers serve as a fundamental interface for autonomous charging and data offloading. However, in docking scenarios involving torpedo-shaped AUVs, marker recognition frequently fails due to severe occlusion and geometric distortion, creating a bottleneck for reliable IoUT maintenance. To address this challenge, this study proposes a monocular Next-Best-View (NBV) planning framework that operates independently of depth information. By leveraging prior geometric knowledge of cooperative targets, the proposed method utilizes a single RGB image from a failed recognition attempt to generate a set of candidate viewpoints. A novel scoring function is introduced to quantify recognizability by comprehensively evaluating visibility, projection flatness, and self-occlusion constraints. Furthermore, an end-to-end network integrating a Vision Transformer (ViT) and U-Net is designed to predict scores for these candidates, effectively capturing both global context and local features. Both simulation and real-world experiments demonstrate that the proposed method significantly enhances real-time performance while maintaining high recognition accuracy, offering an efficient, low-cost active vision solution for AUV docking.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2327-4662
Last Modified:	01 Sep 2026 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/189272

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