轻小型机载激光雷达点云处理关键技术

Key technologies of point cloud processing for light and small airborne LiDAR

针对轻小型机载激光雷达点云数据的高程精度难以满足1∶500大比例尺地形图生产要求的问题,本文采用一种新的层次结构多模型组合滤波方法,进行分类提取地面点。系统性地介绍了机载激光雷达的测量原理、点云数据采集和处理方法,并根据轻小型激光雷达点云数据厚度大、植被密集区域地面点稀少等特征,重点研究了地面点分类提取方法,将多个滤波模型重新组合,能够有效提高分类后地面点的高程精度。对地面点进行重采样后提取高程点用于绘制地形图等高线,该方法兼顾了等高线的精度和美观度。

Aiming at the problem that the elevation accuracy of light and small airborne LiDAR point cloud data is difficult to meet the production requirements of 1∶500 large-scale topographic maps,this paper adopts a new hierarchical multi-model combined filtering method to classify and extract ground points.This paper systematically introduces the measurement principle of airborne LiDAR,the collection and processing methods of point cloud data,and focuses on the classification and extraction methods of ground points according to the characteristics of light and small LiDAR,such as large thickness of point cloud data and few ground points in dense vegetation areas.The new combination of multiple filtering models can effectively improve the elevation accuracy of ground points after classification.After resampling the ground points,the elevation points are extracted and used to draw contour lines of topographic maps.This method perfectly takes into account the accuracy and aesthetics of contour lines.