Sonar-MASt3R：在浑浊、无结构环境中实时优化声学融合
Sonar-MASt3R: Real-Time Opti-Acoustic Fusion in Turbid, Unstructured Environments
AI总结：
本文提出Sonar-MASt3R方法，通过实时提取光学相机数据的密集对应关系并与声学3D重建的几何线索结合，提升在浑浊环境中的鲁棒性。
摘要：
水下干预是多个海洋领域的重要能力，具有众多工业、科学和防御应用。然而，现有干预操作中的感知系统依赖于光学相机数据，在能见度差或光照条件差时受限。先前工作研究了优化声学融合方法，利用声纳数据解决相机数据的深度模糊问题，同时利用相机数据解决声纳数据的仰角模糊问题。然而，现有方法无法在实时条件下实现密集的3D重建，且很少有研究报告这些方法在浑浊环境中的应用结果。本文提出优化声学融合方法Sonar-MASt3R，利用MASt3R实时提取光学相机数据的密集对应关系，并与声学3D重建的几何线索结合，以确保在浑浊条件下的鲁棒性。使用在浑浊值从<0.5到>12 NTU的opti-acoustic eye-in-hand配置记录的数据进行实验，结果表明该方法在浑浊环境中的鲁棒性优于基线方法。
英文摘要：
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.
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。
