迈向多功能光学-声学传感器融合与体积分层映射
Towards Versatile Opti-Acoustic Sensor Fusion and Volumetric Mapping
AI总结：
本文提出融合立体声纳与单目相机的体积分层映射框架，以应对不同能见度条件下的安全导航问题，通过解决声纳视场重叠带来的垂直模糊问题，生成可靠的3D点云。
摘要：
准确的3D体积分层映射对于在障碍物丰富的环境中运行的自主水下车辆至关重要。基于视觉的感知提供高分辨率数据，但在混浊条件下失效，而声纳在光照和混浊条件下鲁棒但存在低分辨率和垂直模糊问题。本文提出一个融合立体声纳对与单目相机的体积分层映射框架，以在不同能见度条件下实现安全导航。重叠的声纳视场解决垂直模糊问题，生成每个时间步的完全定义的3D点云。该框架在相机图像中识别感兴趣区域，将其与对应的声纳返回关联，并结合声纳距离与相机推导的垂直线索生成额外的3D点。每个3D点被赋予一个反映其可靠性的置信度值。这些置信度加权的点通过高斯过程体积分层映射框架融合，优先考虑最可靠的测量值。与其他光学-声学和声纳方法的实验比较以及在码头环境中的实地测试表明，该方法在捕捉复杂几何结构和在清晰和混浊条件下保持关键信息以支持机器人导航方面是有效的。我们的代码是开源的以支持社区采用。
英文摘要：
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.
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。
