关键词: 点云, 反距离加权插值, 自适应阈值分割, 欧氏聚类, 特征属性筛选, 语义关联

Abstract: At present, automatic driving technology has become one of the development direction of the future intelligent transportation system. The high-precision map, which is an important supplement of the on-board sensors under the condition of shielding or the restriction of observing distance, provides a priori information for high-precision positioning and path planning of the automatic driving with the level of L3 and above. The position and semantic information of the road markings, such as the absolute coordinates of the solid line and the broken line, are the basic components of the high-precision map. In this paper, scan lines are extracted from the vehicle-borne laser point cloud data, and the road surfaces are extracted from scan lines according to the mutation of the geometry of road edge. On this basis, the road surface point cloud image is transformed into raster image with a certain resolution by using the method of inverse distance weighted interpolation, and the grid image is converted into binary image by using the method of adaptive threshold segmentation based on the integral graph. Then the method of the Euclidean clustering is used to extract the road markings point cloud from the binary image. Semantic information can be extracted from the road markings point cloud using the method of the characteristic attribute selection. Finally, semantic association is established between the traffic markings and the traffic regulation.

Key words: point cloud, inverse distance weighted interpolation, adaptive threshold segmentation, Euclidean clustering, feature attribute selection, semantic correlation