Generating 3D point cloud (PC) data from noisy sonar measurements is a problem that has potential applications for bathymetry mapping, artificial object inspection, mapping of aquatic plants and fauna as well as underwater navigation and localization of vehicles such as submarines. Side-scan sonar sensors are available in inexpensive cost ranges, especially in fish-finders, where the transducers are usually mounted to the bottom of a boat and can approach shallower depths than the ones attached to an Uncrewed Underwater Vehicle (UUV) can. However, extracting 3D information from side-scan sonar imagery is a difficult task because of its low signal-to-noise ratio and missing angle and depth information in the imagery. Since most algorithms that generate a 3D point cloud from side-scan sonar imagery use Shape from Shading (SFS) techniques, extracting 3D information is especially difficult when the seafloor is smooth, is slowly changing in depth, or does not have identifiable objects that make acoustic shadows. This paper introduces an efficient algorithm that generates a sparse 3D point cloud from side-scan sonar images. This computation is done in a computationally efficient manner by leveraging the geometry of the first sonar return combined with known positions provided by GPS and down-scan sonar depth measurement at each data point. Additionally, this paper implements another algorithm that uses a Convolutional Neural Network (CNN) using transfer learning to perform object detection on side-scan sonar images collected in real life and generated with a simulation. The algorithm was tested on both real and synthetic images to show reasonably accurate anomaly detection and classification.
翻译:从噪音声纳测量中生成 3D 点云(PC) 数据是一个问题,它有可能用于测深、人工物体检查、水生植物和动物的测绘以及水下导航和潜水潜水器等车辆的定位。侧扫描声纳传感器以低廉的成本范围提供,特别是在鱼鳍中,在鱼鳍中,传感器通常安装在船底,并且能够接近较浅深度的海底,而未渗入水下潜水器(UV)则可以。然而,从侧扫描声纳图像中提取 3D 信息是一项艰巨的任务,因为它的信号到噪音比率低,以及图像中缺少角度和深度信息。由于大多数从侧扫描声图中产生 3D 点云的算法都使用沙丁(SFS) 技术的形状,在海底平滑、深度变化缓慢、或没有可识别到声波阴影的物体。本文介绍了一种高效的算法,从侧向侧扫描图像产生稀薄的 3D 3CN 点, 以及图像在图像中缺少的角度和角度缺失的角度和深度信息。由于每次测深处的测算,通过测算,通过测算结果,通过测算,通过测算和测算结果,通过测算结果,通过测算结果,通过测算结果,通过测算结果,通过测算结果到测算结果,通过测测算结果测算结果测测算结果测测测测测测测测测测测测测测到,结果到另一个数据到另一个。