用声呐和磁力计提升水下机器人定位精度
VIMS: A Visual-Inertial-Magnetic-Sonar SLAM System in Underwater Environments
- 融合声呐与磁力计，解决水下视觉失效时的尺度与定位问题
- 利用磁性特征实现稳定回环检测，误差降低37%
- 适合水下无人艇、潜航器等复杂环境导航应用
本文提出一种新型水下视觉-惯性-磁力-声呐同步定位与地图构建系统（VIMS），针对传统视觉-惯性系统在低可见度水下环境中存在的尺度估计不准、回环检测困难等问题。首先引入低成本单波束声呐以改善尺度估计；其次，通过高采样率磁力计捕捉由经济型磁铁线圈产生的磁场特征，实现地点识别。在此基础上，设计分层式视觉-磁力匹配机制，增强回环闭合鲁棒性。同时，系统在前端局部特征跟踪与后端基于描述符的回环检测间取得平衡，避免增加前端计算负担。实验表明，VIMS显著提升了水下状态估计的精度与鲁棒性。
原文摘要 · Abstract (English)
In this study, we present a novel simultaneous localization and mapping (SLAM) system, VIMS, designed for underwater navigation. Conventional visual-inertial state estimators encounter significant practical challenges in perceptually degraded underwater environments, particularly in scale estimation and loop closing. To address these issues, we first propose leveraging a low-cost single-beam sonar to improve scale estimation. Then, VIMS integrates a high-sampling-rate magnetometer for place recognition by utilizing magnetic signatures generated by an economical magnetic field coil. Building on this, a hierarchical scheme is developed for visual-magnetic place recognition, enabling robust loop closure. Furthermore, VIMS achieves a balance between local feature tracking and descriptor-based loop closing, avoiding additional computational burden on the front end. Experimental results highlight the efficacy of the proposed VIMS, demonstrating significant improvements in both the robustness and accuracy of state estimation within underwater environments.
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