Road marking detection and extraction method based on neighborhood density and Kalman filter

doi:10.7523/j.ucas.2022.024

Abstract

Abstract: This paper presents a methodology for detection and extraction of road marking based on neighborhood point density and Kalman filter from mobile laser scanning in three steps:1) Segmenting road point clouds into scan lines; 2) Generating convolution kernel based on point density in neighborhood for extracting road marking contour points; 3) Fitting contour line using least squares algorithm and completing omitted road markings using Kalman filter. A quantitative validation shows that the average deviation of center points and orientation are 0.04 m and 0.04°, respectively, and the average completeness of results are 99.69%. The proposed method reduces the influence of unevenly distributed points on road marking extraction and improves the overall extraction completeness.

Key words: mobile laser scanning, point clouds, convolution, road markings, Kalman filter

CLC Number:

TP75

LI Xiaoyu, ZHOU Mei, WANG Jinhu, YAO Qiangqiang. Road marking detection and extraction method based on neighborhood density and Kalman filter[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(6): 800-809.

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