Road detection from airborne LiDAR point clouds adaptive for variability of intensity data

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• 作者：Yong Li^a ; ^{liyong@hhu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Bin Yong^a ; ^{yongbin_hhu@126.com" class="auth_mail" title="E-mail the corresponding author} ; Huayi Wu^b ; Ru An^c ; Hanwei Xu^c
• 关键词：Road detection ; LiDAR ; Histogram ; Roughness ; Mathematical morphology
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：126
• 期：23
• 页码：4292-4298
• 全文大小：5450 K

文摘

This paper presents a novel algorithm of road detection from airborne LiDAR point clouds adaptive for variability of intensity data of road network. First, the point cloud topology is constructed using a grid index structure which facilitate spatial searching and preserves the accuracy of raw data without interpolation, and a LiDAR filtering algorithm is employed to distinguish the ground points from non-ground points. Second, road candidates are identified in the derived ground points by segmentation based on local intensity distribution histogram. Finally, the ultimate road point sets are verified by global inference based on the roughness and area of the road candidate point sets. The roughness of candidate point sets are calculated based on morphological gradients in consideration of the characteristics of roads compared to other non-road ground areas such as grass land and bare ground. The experimental results using practical data in complex environment demonstrate that this algorithm is able to automatically detect roads adaptive for the variability of intensity data of road network. Other non-road ground areas such as grass land and bare ground can be efficiently eliminated.