Extraction method of road boundary based on three-dimensional LiDAR
To accurately extract road boundaries and provide a foundation for road management and intelligent transportation planning,this paper proposed an extraction method for road boundaries based on three-dimensional(3D) light detection and ranging (LiDAR). The specific implementation steps were: In terms of the collected laser point cloud image,the noise in the point cloud image was removed by judging the average height difference between a point cloud in the image and its neighboring points. By combining laser ray features with plane model fitting methods,ground point clouds were segmented from the denoised point cloud in two stages,including coarse segmentation and fine segmentation. The height difference, smoothness,and angle of neighboring points in the ground point cloud were taken as road boundaries to extract features and construct a multi-feature fusion strategy,which could determine whether the points in the point cloud are road boundary points. A quadratic parabolic model for road boundaries was constructed,and the boundary was extracted by fitting the model coefficients with the least squares method. To verify the effectiveness of the proposed method,boundary extraction experiments were conducted on a certain section of the road by employing the proposed method. The experimental results indicate that this method can effectively extract road boundaries with high accuracy,and this method shows good performance in problems of road boundary extraction,strong robustness and applicability. This method can meet the needs of intelligent transportation planning and autonomous navigation of unmanned vehicles.
light detection and ranging (LiDAR)multi-feature fusionground segmentationboundary characteristicsboundary extractionboundary point fitting