The recent development of high-precision subsea optical scanners allows for 3D keypoint detectors and feature descriptors to be leveraged on point cloud scans from subsea environments. However, the literature lacks a comprehensive survey to identify the best combination of detectors and descriptors to be used in these challenging and novel environments. This paper aims to identify the best detector/descriptor pair using a challenging field dataset collected using a commercial underwater laser scanner. Furthermore, studies have shown that incorporating texture information to extend geometric features adds robustness to feature matching on synthetic datasets. This paper also proposes a novel method of fusing images with underwater laser scans to produce coloured point clouds, which are used to study the effectiveness of 6D point cloud descriptors.

翻译：近年来高精度海底光学扫描仪的发展，使得3D关键点检测器和特征描述符能够被应用于海底环境点云扫描数据中。然而，目前文献中缺乏全面的综述来确定在这些具有挑战性的新型环境中应使用的最佳检测器与描述符组合。本文旨在通过使用商用水下激光扫描仪采集的具有挑战性的实地数据集，确定最优检测器/描述符对。此外，研究表明，融合纹理信息以扩展几何特征可增强合成数据集上特征匹配的鲁棒性。本文还提出了一种将图像与水下激光扫描融合生成彩色点云的新方法，并利用该点云研究6D点云描述符的有效性。