Due to the weak ability of the water surface to reflect laser and the interference caused by water surface fluctuation, traditional laser simultaneous localization and mapping (SLAM) methods suffer from low positioning accuracy and poor robustness for unmanned boats in nearshore water scenarios. To solve this issue, a laser SLAM method based on embankment feature extraction (EF-SLAM) has been proposed in this study. First, EF-SLAM introduced stable water edge points, which are reflected from the shoreline and are distributed within a consistent range of water surface elevations, for matching. This is done to reduce the elevation estimation errors of the lidar odometer in nearshore water scenarios. Then, a water-edge-point extraction method based on point cloud forward projection was developed. Subsequently, a feature association and matching approach between frames and local maps was employed to facilitate the matching of water edge points. Additionally, a residual distance calculation for water edge point to water edge point distances was constructed to estimate the relative pose changes between radar frames. Finally, experiments were conducted using the USVinland public dataset and real-world data from the Qingdao Guzhenkou dataset. Results demonstrate that EF-SLAM effectively mitigates pose drift in odometer readings. Moreover, it exhibits higher positioning accuracy and improved robustness than mainstream laser-based SLAM algorithms.