Supervised Visual Docking Network for Unmanned Surface Vehicles Using Auto-labeling in Real-world Water Environments

2025-03-05

AI Summary

Autonomous, precise docking of unmanned surface vehicles (USVs) in real-world aquatic environments remains challenging due to reliance on manual intervention, external positioning systems (e.g., GPS, RTK), or artificial markers—limiting operational robustness and scalability. Method: This paper proposes an end-to-end visual pose estimation framework enabling fully autonomous, marker-free, calibration-free docking without human annotation. It integrates automatic data acquisition, a Neural Docking Pose Estimator (NDPE)—a deep learning model that directly regresses 6-DoF docking pose from monocular imagery—and pose-based visual servoing (PBVS) control. Crucially, NDPE is trained via a self-supervised, automatically annotated vision-based docking paradigm leveraging real-water trajectory priors. Results: Extensive experiments in dynamic riverine and port environments demonstrate strong robustness against distance variations and velocity disturbances. The system achieves reliable, human-out-of-the-loop autonomous docking—eliminating dependence on external infrastructure, manual labeling, or handcrafted features.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Unmanned Surface Vehicles (USVs) are increasingly applied to water operations such as environmental monitoring and river-map modeling. It faces a significant challenge in achieving precise autonomous docking at ports or stations, still relying on remote human control or external positioning systems for accuracy and safety which limits the full potential of human-out-of-loop deployment for USVs.This paper introduces a novel supervised learning pipeline with the auto-labeling technique for USVs autonomous visual docking. Firstly, we designed an auto-labeling data collection pipeline that appends relative pose and image pair to the dataset. This step does not require conventional manual labeling for supervised learning. Secondly, the Neural Dock Pose Estimator (NDPE) is proposed to achieve relative dock pose prediction without the need for hand-crafted feature engineering, camera calibration, and peripheral markers. Moreover, The NDPE can accurately predict the relative dock pose in real-world water environments, facilitating the implementation of Position-Based Visual Servo (PBVS) and low-level motion controllers for efficient and autonomous docking.Experiments show that the NDPE is robust to the disturbance of the distance and the USV velocity. The effectiveness of our proposed solution is tested and validated in real-world water environments, reflecting its capability to handle real-world autonomous docking tasks.

Problem

• Achieving precise autonomous docking for USVs without human control.
• Developing auto-labeling for supervised learning in USV docking.
• Enhancing real-world water environment docking accuracy and robustness.

Innovation

• Auto-labeling pipeline for supervised learning
• Neural Dock Pose Estimator without feature engineering
• Real-world water environment docking validation