Abstract
With the acceleration of urbanization, urban road networks are being extended and updated at an alarming rate. How to extract the boundary information of the urban road network efficiently and scientifically to support fine urban management has become a difficult problem. Vehicle-Borne Mobile Mapping System can quickly and efficiently obtain 3D road data, and further realize the extraction of urban road boundary. Based on the tracking information of the Vehicle-Borne Mobile Mapping System, this paper extracts contiguous laser scanning lines from vehicle-borne mobile laser scanning data. By further analyzing the spatial geometric distribution features of the point clouds on different terrain of scan lines, this paper proposes an adaptive window clustering classification method based on the scan line index to solve the problem of the automatic extraction of urban road curb boundary. Experiments are carried out on two point clouds data obtained by the vehicle-borne mobile measurement system. And the accuracy of the extraction of curbs boundary is 99%. The experiment results show that this method can effectively reduce the interference of road noise points as well as the influence of facade features on the boundary extraction of curbs and adapt to different shapes and distribution conditions of urban roads curbs.
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References
Fang LN, Lu LJ, Zhao ZY, Wang YY, Chen CC (2020) Road boundaries extraction from mobile laser scanning point clouds based on discrete point snake. Acta Geodaetica et Cartographica Sinica 49(11):1438–1450
Fang LN, Yang BS (2013) Automated extracting structural roads from mobile laser scanning point clouds. Acta Geodaetica et Cartographica Sinica 42(2):260–267
Hui ZY, Hu YJ, Kang YF (2018) Road point cloud extraction algorithm based on reflection intensity skewness balancing. Laser and Optoelectronics Progress 55(2):022801
Liu RF, Wang P, Yan ZJ, Lu XS (2020) Hierarchical classification of polelike objects in mobile laser scanning point clouds. The Photogrammetric Record 35(169):81–107, DOI: https://doi.org/10.1111/phor.12307
Liu XY, Zhang JQ, Liu N, Che YH, Zhang CS (2021) Urban road extraction based on morphological filtering and trajectory detection. Laser and Optoelectronics Progress 1–14
Lu XS, Huang L (2007) Grid method on building information extraction using laser scanning data. Geomatics and Information Science of Wuhan University 32(10):852–885
Ma L, Li Y, Zhong Z, Chapman MA (2019a) Generation of horizontally curved driving Lines in HD maps using mobile laser scanning point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12(5):1572–1586, DOI: https://doi.org/10.1109/JSTARS.2019.2904514
Ma XJ, Liu RF, Cai YN (2019b) A road boundary extraction method from point clouds based on curb features. Remote Sensing Information 34(2):80–85
Pu S, Rutzinger M, Vosselman G, Elberink SO (2011) Recognizing basic structures from mobile laser scanning data for road inventory studies. ISPRS Journal of Photogrammetry and Remote Sensing 66(6):S28–S39, DOI: https://doi.org/10.1016/j.isprsjprs.2011.08.006
Serna A, Marcotegui B (2013) Urban accessibility diagnosis from mobile laser scanning data. ISPRS Journal of Photogrammetry & Remote Sensing 84(84):23–32
Sui LC, Zhu JF, Zhong MQ, Wang X, Kang JM (2021) Extraction of road boundary from MLS data using laser scanner ground trajectory. Open Geosciences 13(1):690–704, DOI: https://doi.org/10.1515/geo-2020-0264
Tan B, Zhong RF, Li Q (2012) Objects classification with vehicle-borne laser scanning data. Journal of Remote Sensing 16(1):58–66
Wang MY, Liu RF, Lu XS, Ren HW, Chen M (2020a) The use of mobile lidar data and gaofen-2 image to classify roadside trees. Measurement Science and Technology 31(12):5005, DOI: https://doi.org/10.1088/1361-6501/aba322
Wang F, Liu RF, Ren HW (2020b) Multi-stage vehicle-mounted laser point cloud registration using road target features. Journal of Surveying and Mapping Science and Technology 37(05):496–502
Wataru F, Takuma I, Kyoichi T, Minoru K (2020) Lateral localization via lidar-based road boundary extraction on community roads. International Journal of Automotive Engineering 11(3):116–123, DOI: https://doi.org/10.20485/jsaeijae.11.3_116
Yu JY, Lu XS, Tian MY, Chen TO, Chen CF (2021) Automatic extrinsic self-calibration of mobile lidar systems based on planar and spherical features. Measurement Science and Technology 32(6):5170, DOI: https://doi.org/10.1088/1361-6501/abecec
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This work was supported by the National key research and development program (2018YFB1600302); National Natural Science Foundation of China (42001414); and Shandong Provincial Natural Science Foundation, China (ZR2019BD033).
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Ren, H., Liu, R., Wang, F. et al. Automatic Extraction Method of Urban Road Curb Boundary from Vehicle-Borne Laser Point Clouds. KSCE J Civ Eng 26, 3560–3569 (2022). https://doi.org/10.1007/s12205-022-1540-0
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DOI: https://doi.org/10.1007/s12205-022-1540-0