Urban road marking extraction method based on vehicle-mounted point cloud data
In response to the problem of missing road markings faced by current urban road marking extraction methods,this paper proposed a method for extracting urban road markings based on vehicle-mounted point cloud data.The process of extracting road markings by using this method was as follows:Firstly,angle-constrained segmentation of road point cloud data was constructed,and an improved triangulation irregular network encryption algorithm was used to filter the point cloud data to obtain road ground points.Secondly,operations such as slicing,intensity histogram rendering,and region growing were implemented on the road ground points,and template matching was used to perform reasonable template matching operations on the extracted road marking point cloud.Finally,different vectorization schemes were applied to process the extracted road markings of different types,completing the extraction of road markings from road elements.The experiments on measured point cloud data show that the extraction accuracy of the proposed method is above 95%,and the errors of each axis are below 0.002.The proposed method can effectively achieve road marking extraction,ensuring clear,complete,and defect-free extracted road markings.The extraction rate and geometric verification accuracy of road markings are high.
intelligent holographic surveying and mappingvehicle-mounted point cloud dataroad markingtriangular network encryptionregion growingtemplate matching
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