A Structural Road Extraction Method Based on Normal Vectors Similarity of Point Clouds

doi:10.13474/j.cnki.11-2246.2018.0352

Abstract

Abstract:

With the distribution characteristics of point clouds in structural roads, a road extraction method based on similarity of point cloud vector is proposed. The method firstly filters the original point cloud to remove the non-ground point interference, and then uses the principal component analysis method to estimate the local normal vector and the curvature value of each laser foot point after filtering the ground point cloud. As a constraint condition, degree is used to segment the road surface point cloud using an improved region growing algorithm. Using the vehicle point cloud data under two different scenarios to carry out experiments, the completeness and accuracy of road extraction are above 93%. The experimental results show that the accuracy and completeness of the road extracted by this method are not affected by the width and shape of the road. It is applicable to the extraction of structured roads in urban environments.

Key words: point cloud, road extraction, principal component analysis, normal vector, region growing

CLC Number:

AN Yaojun, CHEN Xiaoxuan, SUI Lichun, ZHOU Rongrong. A Structural Road Extraction Method Based on Normal Vectors Similarity of Point Clouds[J]. 测绘通报, 2018, 0(11): 69-72.

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