全自动侧扫声呐SLAM框架
A Fully-automatic Side-scan Sonar SLAM Framework
AI总结：
提出一种全自动侧扫声呐SLAM框架，通过自动检测和稳健匹配关键点优化AUV位姿轨迹，在Hugin AUV真实数据上验证可减少航位推算漂移。
摘要：
侧扫声呐（SSS）是一种轻量级声学传感器，常部署在自主水下航行器（AUVs）上，用于提供高分辨率的海底图像。然而，在缺乏三维测深信息和侧扫图像中判别性特征的情况下，利用侧扫图像进行同时定位与建图（SLAM）仍然是一个挑战。为解决这一问题，我们提出了一种基于特征的侧扫声呐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来优化AUV的位姿轨迹。所提出的方法在由Hugin AUV采集的真实数据上进行了评估，使用手动标注的关键点和来自多波束测深仪（MBES）的三维测深网格作为地面真值参考。实验结果表明，我们的方法能够减少航位推算系统的漂移。该框架已公开供社区使用。
英文摘要：
Side-scan sonar (SSS) is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high-resolution seafloor images. However, using side-scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side-scan 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 from the dead-reckoning system. The framework is made publicly available for the benefit of the community.
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