Side-scan sonar (SSS) is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUV) to provide high-resolution seafloor image. However, using side-scan images to perform simultaneous localization and mapping (SLAM) remains a challenge due to lack of 3D bathymetric information and the lack of discriminant features in the sidescan images. To tackle this, we propose a feature-based SLAM framework using side-scan sonar, which is able to automatically detect and robustly match keypoints between paired side-scan images. We then use the detected correspondences as constraints to optimize the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually-annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that our approach is able to reduce drifts compared to the dead-reckoning system. The framework is made publicly available for the benefit of the community.

翻译：侧扫声纳（SSS）是一种轻量级声学传感器，常用于自主水下航行器（AUV）上以获取高分辨率海床图像。然而，由于缺乏三维地形信息以及侧扫图像中判别性特征的不足，利用侧扫图像进行同步定位与地图构建（SLAM）仍是一项挑战。为此，我们提出了一种基于特征的侧扫声纳SLAM框架，该框架能够自动检测并稳健匹配成对侧扫图像之间的关键点。随后，我们利用检测到的对应关系作为约束条件优化AUV位姿轨迹。所提方法在Hugin AUV采集的真实数据上进行了评估，以人工标注的关键点和多波束回声测深仪（MBES）生成的三维地形网格作为地面真值参考。实验结果表明，与纯惯性航位推算系统相比，我们的方法能够有效减小漂移。为促进社区发展，该框架已开源发布。