A point cloud registration method for UAV and TLS LiDAR based on ground features and the relationship of tree positions
[Objective]UAV LiDAR and TLS LiDAR work in different ways,resulting in the lack of in-forest information in the UAV point cloud and the lack of forest canopy information in the TLS point cloud,and it is difficult for the LiDAR point cloud of a single platform to completely describe the three-dimensional vertical structure of the forest,and the fusion of the two is beneficial for eliminating the scanning blindness of each,and for estimating the more accurate parameters of the forest structure.Based on this,a markerless automated alignment method based on the relationship between ground features and tree positions is proposed.[Method]Quercus mongolica and Pinus sylvestris in Harbin Urban Forestry Demonstration Base were selected as the research objects,and DJI Zenmuse L1 LiDAR equipment and Faro Focus3D X330 3D laser scanner were used to obtain the UAV and TLS LiDAR point clouds in the sample plots,respectively.Firstly,the improved progressive encrypted triangular mesh filtering algorithm was utilized to extract the ground point cloud from the UAV point cloud and the TLS point cloud respectively,and based on the similar fast point feature histogram features of the two,the random sampling consistency algorithm was used to obtain the initial alignment parameters to complete the initial alignment.Then,the horizontal projection positions of the tree trunk point clouds at the same altitude were extracted from the initially aligned UAV point cloud and TLS point cloud as the alignment primitives to construct the irregular triangular mesh,and search for the pairs of triangles with the same name based on the principle of angular similarity of triangles.Finally,the singular value decomposition method was used to obtain the rotational translation parameters to complete the fine alignment.[Result]The average horizontal offset distance of the corresponding trees in Q.mongolica sample site was 0.173 m,and the average horizontal offset distance of the corresponding trees in P.sylvestris sample site was 0.283 m,and the point clouds of the two sample sites had achieved high alignment accuracy.[Conclusion]The point cloud alignment method proposed in this study effectively realizes the alignment of UAV point cloud data and TLS point cloud data in forest areas,and the fusion of the two provides a data basis for rapid and complete acquisition of forest tree configuration information,thus promoting the joint application of multi-source LiDAR technology in forest tree 3D reconstruction and fine forest resource survey.
FPFHtree position relationshipirregular triangulationUAV point cloudTLS point cloud
NETL
NSTL
万方数据
