Airborne laser radar point cloud data conversion method and system based on attribute matching

By decomposing and dynamically matching airborne LiDAR point cloud data, and utilizing GPU acceleration and KD tree proximity retrieval, the problems of uneven accuracy and large edge difference in airborne LiDAR point cloud data conversion were solved, achieving high-precision point cloud conversion.

CN122085247APending Publication Date: 2026-05-26SHANDONG PROVINCIAL LAND SURVEYING & MAPPING INST
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Patent Information

Application Number
CN202610110583.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-27
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In the existing technology, the conversion methods between airborne LiDAR point cloud data obtained by different calculation methods have uneven accuracy, large differences in the edge connection of point clouds between flight strips, and poor processing effect in areas with abrupt changes in elevation, which cannot meet the requirements for accurate conversion.

Method used

The airborne LiDAR point cloud data conversion method based on attribute matching decomposes the point cloud data into three types: deduplicated point cloud, duplicate point cloud, and point cloud with missing attributes. It uses GPU-accelerated row-by-row matching and selective KD-tree neighbor retrieval to perform dynamic matching, and finally achieves accurate conversion of all airborne LiDAR point clouds.

Benefits of technology

It achieves accurate conversion of airborne LiDAR point cloud data, with a conversion success rate of 100%, and lossless conversion of 99.9999% of the complete point cloud data with attributes. The conversion accuracy is higher and meets real-world requirements.

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Abstract

The invention discloses an airborne laser radar point cloud data conversion method and system based on attribute matching, and relates to the technical field of point cloud data matching. A timestamp, an echo position, echo times, intensity and space coordinate information of airborne LiDAR point cloud data are fully utilized, a point cloud matrix is constructed, and the point cloud data are decomposed into three types of point cloud after deduplication, repeated point cloud and attribute missing point cloud; for different types of point clouds, dynamic matching is carried out through a point cloud matrix, GPU acceleration line-by-line matching is utilized, and matching and conversion are carried out in a selective KD tree proximity retrieval mode. According to the invention, accurate conversion of all airborne LiDAR point clouds can be realized.
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