Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, we curate and release DotsonEast Dataset, a semi-synthetic MBES registration dataset constructed from an autonomous underwater vehicle in West Antarctica. Using this dataset, we systematically benchmark the performance of 2 classical and 4 learning-based methods. The experimental results show that the learning-based methods work well for coarse alignment, and are better at recovering rough transforms consistently at high overlap (20-50%). In comparison, GICP (a variant of ICP) performs well for fine alignment and is better across all metrics at extremely low overlap (10%). To the best of our knowledge, this is the first work to benchmark both learning-based and classical registration methods on an AUV-based MBES dataset. To facilitate future research, both the code and data are made available online.

通过西南极无人潜水器构建的半合成MBES数据集，系统评估了2种经典方法和4种基于学习的方法的性能，结果表明基于学习的方法在粗对齐方面表现良好，并能在高重叠度（20-50%）时一致地恢复粗略变换。相比之下，GICP（ICP的变种）在细对齐方面表现良好，并在极低重叠度（10%）时在所有度量指标上更优。据我们所知，这是第一次在基于AUV的MBES数据集上对基于学习的方法和经典的注册方法进行评估。为促进未来的研究，我们提供了代码和数据的在线资源。

经典和基于学习的多波束点云配准的基准测试