Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

Point cloud thinning is an important data pre-processing method for saving computing resources and improving accuracy of point cloud matching. Aiming at the problems of insufficient feature extraction, poor environmental adaptation, and high computational complexity in existing methods, this study proposes a simplified method based on point cloud salient factors. This method first preprocesses the point cloud data, then calculates the significant factors of the point cloud, and uses the local curvature variance to perform adaptive region division. In the local area, the improved feature farthest point sampling (CIFPS) algorithm is used to classify the point cloud. Perform thinning and finally obtain a simplified point cloud. In order to verify the effectiveness of the method, we conducted a large number of experiments on our own experimental platform and public data sets, and compared it with several related point cloud simplification methods. Experimental results show that the average thinning time of our algorithm on 16-line radar data is 23.67ms, 32-line radar is 50.74ms, and 64-line radar is 87.28ms; the point cloud matching error is relative to that based on farthest point sampling and voxel Sampling and Laplacian sampling were reduced by 44.3%, 46.3%, and 23.7% respectively.

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Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

Cited by 4 publications

References 72 publications

Dynamic clustering transformer network for point cloud segmentation

Dynamic clustering transformer network for point cloud segmentation

Integrating topographic features and patch matching into point cloud restoration for terrain modelling

Adaptive point cloud thinning method based on point cloud saliency factor

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