An Improved LiDAR Registration Alogrithm Based on Point Density Adjustment
Due to the scanning mechanism,the point cloud data collected by line-scan LiDAR presents obvious stripe struc-tures.In order to guarantee the registration results,an improved LiDAR point cloud registration method based on point density ad-justment is proposed.First,the input data are transformed into a supervoxel frame by an octree algorithm and a region growing meth-od.Then,the resampling process is constrained based on the framework,and new points are projected along the normal direction.Then,the distribution of the insertion points are adjusted to minimize an energy function to make the points evenly distributed.Ex-periments show that the algorithm can improve the quality of point cloud and the registration accuracy of classical ICP algorithm and 3D-NDT algorithm.Furthermore,the registration time is reduced.
point cloud resamplingregistrationICP alogrithmNDT alogrithmline-scan LiDAR
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