Impacts of point cloud density reductions on extracting road geometric features from mobile LiDAR data

点云 激光雷达 分割 遥感 测距 点(几何) 密度估算 灵敏度(控制系统) 计算机科学 环境科学 激光扫描 统计 地理 数学 大地测量学 人工智能 激光器 几何学 工程类 光学 物理 估计员 电子工程
作者
Suliman Gargoum,Karim El-Basyouny
出处
期刊:Canadian Journal of Civil Engineering [NRC Research Press]
卷期号:49 (6): 910-924
标识
DOI:10.1139/cjce-2020-0193
摘要

The variation in point cloud density is driven by many different factors. This variation is expected to affect the quality of the information extracted from the point clouds, however, the extent to which these variations impact the ability to accurately extract and assess geometric features of highways from point cloud data are unknown. This paper investigates the impacts of point density reduction on the extraction and assessment of four critical geometric features. The density of light detection and ranging (LiDAR) data was first reduced and the different features were extracted at varying levels of point density and on a selection of different highway segments in Alberta, Canada. The information obtained at lower point density was then compared to what was obtained at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e., reducing the point density to as low as 10% of the original data (30 ppm 2 on the pavement surface) yielded results comparable to what was obtained at 100% density (300 ppm 2 ) In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed. These findings are critical for transportation agencies considering the adoption of LiDAR technology to manage elements of their infrastructure and for researchers developing data processing tools and algorithms for the semantic segmentation of transportation features from remotely sensed point clouds.
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