使用显著性划分的机载激光雷达点云滤波

点云滤波是机载激光雷达点云数据处理的一个关键步骤。针对目前已有算法存在的单一方法在特定地形的点云滤波效果较好，而对于复杂或混合地形滤波效果不理想的现状，提出了一种基于点云网格地面显著性的点云滤波方法。该方法在点云以虚拟网格组织的基础上，使用扫描线高程分割的方式，计算各网格单元的地面显著性指标，依据地面显著性值对网格内的点云进行地形类别划分，对不同类别的网格单元，使用不同的滤波处理手段。该方法与其他经典方法相比，避免了迭代加密过程，在起伏区域使用曲面而非平面来代替局部地形，在不显著增加计算量的情况下，对于复杂和混合地形具有更好的适应性，能够生成可靠性较高的地面点集合。

Airborne LiDAR point cloud filtering using saliency division

Point cloud filtering is one of the key steps of airborne LiDAR point data processing. Most traditional methods obtain satisfactory effects just for several specific terrain types. However, ground filtering on the point cloud of complex or mixed terrain types faces a huge challenge. Therefore, a new point cloud filtering method based on grid ground saliency division was proposed. As the point clouds were organized with virtual grid, a ground saliency division based on elevation was performed on the scanning line of point clouds. For different types of grids, different filtering processes were employed to segment point clouds into ground points and non-ground points according to the ground saliency value. Compared with other classical methods, the proposed method avoids the iterative encryption process, and the curved surface was used to fit local terrain in those undulating area, which has better adaptability in complex and mixed terrains with limited increase of computational cost, and generates a set of ground points with high reliability.