Research on ROV underwater positioning technology based on enhanced visual-inertial SLAM in hydraulic tunnel environments

Abstract

[Objective] With the increasing number of water transfer projects in China, establishing an efficient intelligent inspection system is crucial for the long-term safe operation and maintenance of water transfer projects. Precise positioning within hydraulic tunnels is key and challenging for ROVs to achieve autonomous inspection, while existing ROV underwater positioning method primarily target marine environments, making conventional positioning approaches difficult to apply effectively in hydraulic tunnels.[Methods] An underwater positioning technology based on enhanced visual-inertial SLAM is proposed. Aiming at the problems of limited feature regions and blurred features in tunnel images, an underwater image enhancement network based on DE-MFET is constructed. This network fuses depth information and multi-scale feature enhancement modules to enhance the channel response of important image features, highlight the local edge details of underwater images, and improve the quantity and efficiency of feature matching between SLAM keyframes. To counteract frequent visual odometry failures caused by weak features and strong feature homogeneity in hydraulic tunnels, enhanced visual odometry is fused with inertial navigation data to further correct the ROVs instantaneous pose, improving positioning accuracy in hydraulic tunnels.[Results] Validation using a self-developed hydraulic tunnel dataset, the LSUI and the UIEB public underwater dataset demonstrates that the proposed image enhancement method significantly improves underwater image quality, outperforming other enhancement method in UCIQE, UIQM scores, and ORB feature matching quantity. ROV underwater positioning experiments in Hubei's Ebei Water Resources Allocation Project confirm that the enhanced visual-inertial SLAM method effectively improves positioning accuracy within hydraulic tunnels.[Conclusion] This positioning method exhibits notable superiority and robustness in hydraulic tunnel environments, providing a critical research foundation for intelligent inspection solutions in water transfer projects.

Keywords

vision-inertial SLAM / underwater image enhancement / hydraulic tunnel / ROV underwater positioning / water diversion project / intelligent patrol inspection / information fusion / depth estimation transformer