基于岸堤特征提取的近岸无人船激光SLAM方法

Laser SLAM Method for Nearshore Unmanned Boat Based on Embankment Feature Extraction

针对无人船在近岸水域场景下使用传统激光同步定位与建图(SLAM)方法时,由于水面反射激光的能力较弱和水面波动造成的干扰,定位精度低和鲁棒性差的问题,提出一种基于岸堤特征提取的激光SLAM方法(EF-SLAM)。首先,引入了由岸堤反射且稳定分布在水面高程范围内的水边线点进行匹配,以减小激光雷达里程计在近岸水域场景下的高程估计误差,并由此提出基于点云前视投影的水边线点提取方法。其次,为了实现水边线点的匹配,采用帧到局部地图的特征关联与匹配方式,并构建了水边线点到水边线距离残差,以估计雷达帧间相对位姿变化。最后,基于USVinland公开数据集与青岛古镇口海域实测数据集的实验结果表明,所提EF-SLAM可有效地抑制里程计的位姿漂移,相比主流激光SLAM,具有更高的定位精度与更强的鲁棒性。

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(EFSLAM)has been proposed in this study.First,EFSLAM 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 wateredgepoint 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 realworld data from the Qingdao Guzhenkou dataset.Results demonstrate that EFSLAM effectively mitigates pose drift in odometer readings.Moreover,it exhibits higher positioning accuracy and improved robustness than mainstream laserbased SLAM algorithms.