提出实时水下光声融合方法，提升浑浊环境感知能力
Sonar-MASt3R: Real-Time Opti-Acoustic Fusion in Turbid, Unstructured Environments
- 用MASt3R实时提取相机稠密匹配点，结合声呐几何信息
- 在浊度0.5至12 NTU下均实现稳定三维重建
- 适合水下机器人、海洋探测等浑浊环境应用
水下作业在工业、科研和国防领域具有重要意义，但现有感知系统依赖光学相机，在能见度差或光照不足时性能受限。以往的光声融合方法虽能解决相机深度模糊与声呐俯仰角模糊问题，但难以实现实时稠密三维重建，且极少在浑浊环境中验证。本文提出光声融合方法Sonar-MASt3R，利用MASt3R从相机数据中实时提取稠密对应关系，并结合声呐三维重建的几何线索，增强在浑浊环境下的鲁棒性。实验基于眼手式光声配置，在浊度0.5至12 NTU范围内的数据验证了该方法相比基线方法对浑浊条件的显著适应性。
原文摘要 · Abstract (English)
Underwater intervention is an important capability in several marine domains, with numerous industrial, scientific, and defense applications. However, existing perception systems used during intervention operations rely on data from optical cameras, which limits capabilities in poor visibility or lighting conditions. Prior work has examined opti-acoustic fusion methods, which use sonar data to resolve the depth ambiguity of the camera data while using camera data to resolve the elevation angle ambiguity of the sonar data. However, existing methods cannot achieve dense 3D reconstructions in real-time, and few studies have reported results from applying these methods in a turbid environment. In this work, we propose the opti-acoustic fusion method Sonar-MASt3R, which uses MASt3R to extract dense correspondences from optical camera data in real-time and pairs it with geometric cues from an acoustic 3D reconstruction to ensure robustness in turbid conditions. Experimental results using data recorded from an opti-acoustic eye-in-hand configuration across turbidity values ranging from <0.5 to >12 NTU highlight this method's improved robustness to turbidity relative to baseline methods.
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