融合单目相机与双声呐，实现水下复杂环境的高可靠3D建图。
Towards Versatile Opti-Acoustic Sensor Fusion and Volumetric Mapping
- 用双声呐视角重叠解决高度模糊，生成每帧完整的3D点云。
- 通过相机识别兴趣区域，结合声呐距离与视觉高程生成新3D点。
- 基于置信度加权融合，适合水下机器人在浑浊/清澈环境导航。
在障碍物密集的水下环境中，精确的三维体素建图对自主水下航行器至关重要。视觉感知虽能提供高分辨率数据，但在浑浊条件下失效；而声呐虽抗光照与浑浊，却存在分辨率低和高程模糊问题。本文提出一种体素建图框架，融合一对立体声呐与单目相机，实现不同能见度下的安全导航。重叠的声呐视场可消除高程模糊，每时刻生成完整3D点云。该框架在相机图像中识别兴趣区域，将其与对应声呐回波关联，并结合声呐距离与相机推导的高程信息生成额外3D点。每个3D点附带置信度值以反映其可靠性。这些置信度加权点通过高斯过程体素映射框架融合，优先采用最可靠的测量。与其它光声及声呐方法的对比实验，以及在码头环境的实地测试表明，该方法能有效捕捉复杂几何结构，并在清晰与浑浊条件下均保留对机器人导航至关重要的信息。代码开源，支持社区应用。
原文摘要 · Abstract (English)
Accurate 3D volumetric mapping is critical for autonomous underwater vehicles operating in obstacle-rich environments. Vision-based perception provides high-resolution data but fails in turbid conditions, while sonar is robust to lighting and turbidity but suffers from low resolution and elevation ambiguity. This paper presents a volumetric mapping framework that fuses a stereo sonar pair with a monocular camera to enable safe navigation under varying visibility conditions. Overlapping sonar fields of view resolve elevation ambiguity, producing fully defined 3D point clouds at each time step. The framework identifies regions of interest in camera images, associates them with corresponding sonar returns, and combines sonar range with camera-derived elevation cues to generate additional 3D points. Each 3D point is assigned a confidence value reflecting its reliability. These confidence-weighted points are fused using a Gaussian Process Volumetric Mapping framework that prioritizes the most reliable measurements. Experimental comparisons with other opti-acoustic and sonar-based approaches, along with field tests in a marina environment, demonstrate the method's effectiveness in capturing complex geometries and preserving critical information for robot navigation in both clear and turbid conditions. Our code is open-source to support community adoption.
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