Airborne laser point cloud processing method based on variable-resolution voxel grid and application

Abstract

The invention relates to an airborne laser point cloud processing method based on a variable-resolution voxel grid and application. Firstly, the variable-resolution point cloud compression algorithm can compress the original laser point cloud into the variable-resolution point cloud; the variable-resolution voxel grid obtained based on the regular gridding of the variable-resolution point cloud can cover a large plane range and can also ensure that a central area has a high resolution, and therefore different requirements of processing of different terrain scenes for the plane coverage range and the resolution of the voxel grid can be met at the same time. Secondly, in order to process a voxel grid with a larger size and adopt a larger basic network structure, a three-dimensional semanticsegmentation network of an encoding-decoding structure is built based on sub-stream sparse convolution. The network has relatively high data processing speed and relatively small video memory occupation. The variable-resolution voxel grid can flexibly cope with different terrain scenes, and the robustness of the point cloud classification model is effectively improved.

Description

technical field [0001] The invention relates to the field of remote sensing surveying and mapping, in particular to an airborne laser point cloud data processing method. Background technique [0002] As an important part of the smart earth, 3D geographic data has always been the focus of research in the field of photogrammetry and remote sensing for its rapid acquisition technology and data post-processing methods. As a real-time, fast and active measurement method, laser scanning technology can obtain high-precision 3D point cloud and auxiliary physical properties of the measured target all day, and at the same time, it can obtain the occluded area through the vegetation gap. three-dimensional information. Research on point cloud data compression methods has been an important research topic in the past two decades. Many classic and effective preprocessing algorithms have been used to realize point cloud data preprocessing, such as projection transformation and voxel divisi...

AI Technical Summary

Problems solved by technology

However, based on the existing research methods, point cloud data preprocessing is still in the exploratory stage, mainly because of the variety of point clouds (different point cloud spatial distribution, such as uneven density), the complexity of the scene (the types of scanned data and objects) Various: such as houses, dense forests, artificial ground objects, etc.) and severe occlusion lead to

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