水下环境实时定位与逼真建图，融合声压、视觉与惯性信息。
APVI-SLAM: Real-Time Acoustic-Pressure-Visual-Inertial Localization and Photorealistic Mapping System in Complex Underwater Environment
- 动态加权多传感器数据，失败时自动恢复定位
- 在珊瑚礁场景实现亚米级定位与高保真三维重建
- 首次提供同步多模态水下数据集，适合海洋机器人研究
极端海底环境常导致水下视觉-惯性SLAM中特征退化与估计发散。尽管多普勒速度计（DVL）和压力计可提供辅助约束，但在视觉信号间歇失效时仍难以实现鲁棒的多传感器融合。为此，我们提出APVI-SLAM，一个实时多传感器融合SLAM系统，实现高精度水下定位与逼真三维建图。该方法引入可靠性感知的定位框架，动态调整传感器估计器权重，并采用滑动窗口冻结策略，在跟踪失败时有效恢复，显著提升系统鲁棒性。此外，为实现高保真场景重建，提出基于四叉树引导的增量式水介质建模模块，支持3D高斯优化。针对水下建图缺乏基准的问题，我们还构建了一个同步多模态数据集，用于珊瑚礁巡测。在公开数据集及自建基准上的大量实验表明，APVI-SLAM在实时速度下达到当前最优的定位与重建质量。
原文摘要 · Abstract (English)
Extreme subsea environments often cause severe feature de-gradation and estimator divergence in underwater visual-inertial SLAM. Although sensors like Doppler Velocity Logs (DVL) and pressure gauges provide auxiliary constraints, robust multi-sensor fusion during intermittent visual failure remains challenging. To address this, we present APVI-SLAM, a real-time multi-sensor fusion SLAM system that achieves both accurate underwater localization and photorealistic mapping. Our approach introduces a reliability-aware localization framework that dynamically reweights sensor estimators and employs a sliding-window freezing strategy to recover from tracking failures, substantially enhancing system robustness. Furthermore, for high-fidelity scenes reconstruction, we propose an efficient quadtree-guided mapping module that facilitates incremental water-medium modeling and 3D Gaussian optimization. Recognizing the lack of benchmark for underwater mapping evaluation, we also contribute a coral reef surveying dataset with synchronized multi-modality data. Extensive experiments on public and our proposed benchmarks demonstrate that APVI-SLAM achieves state-of-the-art localization and reconstruction quality at real-time speeds.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。