水下机器人用激光辅助实现连续高精度三维建图，解决弱纹理环境定位难题。
An Underwater, Fault-Tolerant, Laser-Aided Robotic Multi-Modal Dense SLAM System for Continuous Underwater In-Situ Observation
- 融合激光、惯导、压力与双目相机，构建抗干扰多模态感知系统。
- 在传感器部分失效时仍保持100%连续性，轨迹误差仅0.039米。
- 适用于深水洞穴、黑暗水域等复杂场景，建图密度达6922点/立方米。
现有水下SLAM系统在纹理稀疏、几何退化的环境中难以稳定工作，导致跟踪中断与地图稀疏。为此，本文提出Water-DSLAM，一种激光辅助的多传感器融合系统，可在多种复杂水下场景中实现不间断、容错的密集建图。核心创新包括：1）自研水下双目结构光（UBSL）模块，实现高精度3D感知；2）故障容错三子系统架构：①基于声学测速仪和压力计的惯性导航系统（DP-INS），提供高频绝对位姿；②基于迭代扩展卡尔曼滤波（IESKF）的UBSL与DP-INS紧耦合，缓解结构光退化问题；③结合DP-INS与双目相机实现精准初始化与跟踪；3）多模态因子图后端，动态融合异构数据，有效应对传感器异步与部分数据丢失。实验表明，Water-DSLAM在池塘、黑暗水域、16米深溶洞及实地河流中表现优异，轨迹均方根误差为0.039米，连续性达100%，在750立方米水体中实现6922.4点/立方米的密集建图，约为现有方法的10倍。项目开源：https://water-scanner.github.io/
原文摘要 · Abstract (English)
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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