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arXiv · 2504.21826

An Underwater, Fault-Tolerant, Laser-Aided Robotic Multi-Modal Dense SLAM System for Continuous Underwater In-Situ Observation

Yaming Ou·Junfeng Fan·Chao Zhou·Pengju Zhang·Zongyuan Shen·Yichen Fu·Xiaoyan Liu·Zengguang Hou
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Abstract

Existing underwater SLAM systems are difficult to work effectively in texture-sparse and geometrically degraded underwater environments, resulting in intermittent tracking and sparse mapping. Therefore, we present Water-DSLAM, a novel laser-aided multi-sensor fusion system that can achieve uninterrupted, fault-tolerant dense SLAM capable of continuous in-situ observation in diverse complex underwater scenarios through three key innovations: Firstly, we develop Water-Scanner, a multi-sensor fusion robotic platform featuring a self-designed Underwater Binocular Structured Light (UBSL) module that enables high-precision 3D perception. Secondly, we propose a fault-tolerant triple-subsystem architecture combining: 1) DP-INS (DVL- and Pressure-aided Inertial Navigation System): fusing inertial measurement unit, doppler velocity log, and pressure sensor based Error-State Kalman Filter (ESKF) to provide high-frequency absolute odometry 2) Water-UBSL: a novel Iterated ESKF (IESKF)-based tight coupling between UBSL and DP-INS to mitigate UBSL's degeneration issues 3) Water-Stereo: a fusion of DP-INS and stereo camera for accurate initialization and tracking. Thirdly, we introduce a multi-modal factor graph back-end that dynamically fuses heterogeneous sensor data. The proposed multi-sensor factor graph maintenance strategy efficiently addresses issues caused by asynchronous sensor frequencies and partial data loss. Experimental results demonstrate Water-DSLAM achieves superior robustness (0.039 m trajectory RMSE and 100\% continuity ratio during partial sensor dropout) and dense mapping (6922.4 points/m^3 in 750 m^3 water volume, approximately 10 times denser than existing methods) in various challenging environments, including pools, dark underwater scenes, 16-meter-deep sinkholes, and field rivers. Our project is available at https://water-scanner.github.io/.

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Yaming Ou, Junfeng Fan, Chao Zhou, Pengju Zhang, Zongyuan Shen, Yichen Fu, Xiaoyan Liu, Zengguang Hou. 2025-04-30. An Underwater, Fault-Tolerant, Laser-Aided Robotic Multi-Modal Dense SLAM System for Continuous Underwater In-Situ Observation. https://arxiv.org/abs/2504.21826

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