一种用于连续水下原位观测的水下、容错、激光辅助机器人多模态稠密SLAM系统
An Underwater, Fault-Tolerant, Laser-Aided Robotic Multi-Modal Dense SLAM System for Continuous Underwater In-Situ Observation
AI总结：
提出Water-DSLAM，一种激光辅助多传感器融合系统，通过UBSL模块、容错三子系统架构和多模态因子图后端，在复杂水下环境中实现连续原位观测的鲁棒稠密SLAM。
摘要：
现有的水下SLAM系统难以在纹理稀疏和几何退化的水下环境中有效工作，导致跟踪中断和地图稀疏。因此，我们提出了Water-DSLAM，这是一种新颖的激光辅助多传感器融合系统，能够通过三项关键创新在多样化的复杂水下场景中实现不间断、容错的稠密SLAM，从而进行连续的原位观测：首先，我们开发了Water-Scanner，这是一个多传感器融合机器人平台，具有自研的水下双目结构光（UBSL）模块，可实现高精度3D感知。其次，我们提出了一种容错的三子系统架构，结合了：1）DP-INS（DVL和压力辅助惯性导航系统）：基于误差状态卡尔曼滤波器（ESKF）融合惯性测量单元、多普勒测速仪和压力传感器，以提供高频绝对里程计；2）Water-UBSL：一种基于迭代ESKF（IESKF）的UBSL与DP-INS紧耦合，以缓解UBSL的退化问题；3）Water-Stereo：DP-INS与双目相机的融合，用于精确的初始化和跟踪。第三，我们引入了一个多模态因子图后端，可动态融合异构传感器数据。所提出的多传感器因子图维护策略有效解决了由异步传感器频率和部分数据丢失引起的问题。实验结果表明，Water-DSLAM在各种具有挑战性的环境中实现了卓越的鲁棒性（在部分传感器失效期间0.039 m的轨迹RMSE和100%的连续性比率）和稠密建图（在750 m^3水体中6922.4点/m^3，比现有方法稠密约10倍），包括水池、黑暗水下场景、16米深的天坑和野外河流。我们的项目可在https://water-scanner.github.io/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
