Point Cloud Structural Similarity-based Underwater Sonar Loop Detection
Donghwi Jung, Andres Pulido, Jane Shin, and Seong-Woo Kim

TL;DR
This paper introduces a novel 3D point cloud structural similarity method for underwater sonar loop detection that avoids data loss and extensive preprocessing, demonstrating superior performance in diverse underwater environments.
Contribution
The proposed method directly uses 3D sonar point clouds with rotation-invariant similarity, eliminating keypoint detection and training, improving robustness and efficiency in underwater SLAM.
Findings
Achieves highest loop detection performance among compared methods.
Operates without training or extensive preprocessing.
Validated on real-world underwater datasets.
Abstract
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…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Robotics and Sensor-Based Localization · Underwater Acoustics Research
