A road curb points extraction algorithm combined spatial features and measuring distance

doi:10.11947/j.AGCS.2021.20210244

Abstract

Abstract: Extracting accurate road curb is a crucial task for driverless vehicles. However, existing road curb points extraction methods are not robust for sparse 16-ray LiDAR data. This paper presents a road curb points extraction algorithm that combines multiscale spatial features and measuring distance. The points outside the road areas are firstly removed by adopting the random sample consensus (RANSAC) algorithm, then most of the road surface points are removed by judging the horizontal and vertical continuity between points in the same laser beam. According to the measurement model of the road curb points, if the measured distance of the reserved points is within a reasonable distance and the angle between the two horizontal vectors starting with the point is larger than a certain threshold, the point will be identified as the road curb point. Experiments show that the road curb points extraction method proposed in this paper performs better than the other methods under 16-ray LiDAR dataset and meets the real-time requirements for environmental perception of driverless vehicles.

Key words: 16-ray LiDAR, road curb detection, LiDAR calibration, LiDAR data correction, multiscale spatial features

CLC Number:

P237

XU Dong, LIU Jingbin, HUA Xianghong, TAO Wuyong. A road curb points extraction algorithm combined spatial features and measuring distance[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11): 1534-1545.

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