In this letter, we propose a point cloud structural similarity-based loop detection method for underwater Simultaneous Localization and Mapping using sonar sensors. Existing sonar-based loop detection approaches often rely on 2D projection and keypoint extraction, which can lead to data loss and poor performance in feature-scarce environments. Additionally, methods based on neural networks or Bag-of-Words require extensive preprocessing, such as model training or vocabulary creation, reducing adaptability to new environments. To address these challenges, our method directly utilizes 3D sonar point clouds without projection and computes point-wise structural feature maps based on geometry, normals, and curvature. By leveraging rotation-invariant similarity comparisons, the proposed approach eliminates the need for keypoint detection and ensures robust loop detection across diverse underwater terrains. We validate our method using two real-world datasets: the Antarctica dataset obtained from deep underwater and the Seaward dataset collected from rivers and lakes. Experimental results show that our method achieves the highest loop detection performance compared to existing keypointbased and learning-based approaches while requiring no additional training or preprocessing. Our code is available at https://github.com/donghwijung/point_cloud_structural_similarity_based_underwater_sonar_loop_detection.

翻译：本文提出了一种基于点云结构相似性的回环检测方法，用于水下声纳传感器的同步定位与建图。现有的声纳回环检测方法通常依赖于二维投影与关键点提取，这可能导致数据丢失，并在特征稀缺环境中表现不佳。此外，基于神经网络或词袋模型的方法需要大量预处理（如模型训练或词典构建），降低了对新环境的适应能力。为解决这些问题，本方法直接利用三维声纳点云而无需投影，并基于几何、法向量和曲率计算逐点结构特征图。通过利用旋转不变性相似度比较，所提方法无需关键点检测，并确保在不同水下地形中实现鲁棒的回环检测。我们使用两个真实数据集验证了本方法：从深水区获取的南极数据集，以及在河流与湖泊中采集的Seaward数据集。实验结果表明，与现有的基于关键点及基于学习的方法相比，本方法在无需额外训练或预处理的情况下，实现了最高的回环检测性能。代码发布于https://github.com/donghwijung/point_cloud_structural_similarity_based_underwater_sonar_loop_detection。