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)是一种轻量级的声学传感器,经常用于自主水下车辆(AUV)上,提供高分辨率的海底图像。然而,利用侧扫图像执行同时定位和地图构建(SLAM)仍然是一个挑战,因为缺乏三维地形信息和侧扫图像中缺乏区别性特征。为了解决这个问题,我们提出了一种基于特征的 SLAM 框架,使用侧扫声纳,能够自动检测和鲁棒地匹配成对的侧扫图像之间的关键点。然后,我们使用检测到的对应关系作为约束来优化 AUV 姿态轨迹。我们使用 Hugin AUV 收集的真实数据进行评估,参考地面真值,包括手动注释的关键点和多波束回声声纳(MBES)的三维地形网格。实验结果表明,与航位推测系统相比,我们的方法能够减少漂移。该框架已公开发布,以造福社会。