The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

Filtering of airborne laser scanning (ALS) point clouds into ground and nonground points is a core postprocessing step for ALS data. A hierarchical filtering method, which has high operating efficiency and accuracy because of the combination of multiscale morphology and progressive triangulated irregular network (TIN) densification (PTD), is proposed. In the proposed method, the grid is first constructed for the ALS point clouds, and virtual seed points are set by analyzing the shape and elevation distribution of points within the grid. Then, the virtual seed points are classified as ground or nonground using the multiscale morphological method. Finally, the virtual ground seed points are utilized to generate the initial TIN, and the filter is completed by iteratively densifying the initial TIN. We used various ALS data to test the performance of the proposed method. The experimental results show that the proposed filtering method has strong applicability for a variety of landscapes and, in particular, has lower commission error than the classical PTD filtering method in urban areas.