Direct Forward-Looking Sonar Odometry: A Two-Stage Odometry for Underwater Robot Localization - Wenhao Xu; Jianmin Yang; Jinghang Mao; Haining Lu; Changyu Lu; Xinran Liu (Remote Sensing, 2025)
Underwater robots require fast and accurate localization results during challenging near-bottom operations. However, commonly used methods such as acoustic baseline localization, dead reckoning, and sensor fusion have limited accuracy. The use of forward-looking sonar (FLS) images to observe the seabed environment for pose estimation has gained significant traction in recent years. This paper proposes a lightweight front-end FLS odometry to provide consistent and accurate localization for underwater robots. The proposed direct FLS odometry (DFLSO) includes several key innovations that realize the extraction of point clouds from FLS images and both image-to-image and image-to-map matching. First, an image processing method is designed to rapidly generate a 3-D point cloud of the seabed using FLS image, enabling pose estimation through point cloud matching. Second, a lightweight keyframe system is designed to construct point cloud submaps, which utilize historical information to enhance global pose consistency and reduce the accumulation of image-matching errors. The proposed odometry algorithm is validated by both simulation experiments and field data from sea trials.