Road surface and its center line and boundary lines detection using terrestrial Lidar data

“…For other extraction methods based on intensity images, which are generated from laser data, Husain et al [1] semi-autumnally extracted road boundary lines based on intensity images generated from laser points. Balado et al [43] used planar segmentation based on a split and merge operation method with geometric and topological information.…”

Section: Previous Workmentioning

confidence: 99%

“…Road edges are an indispensable part of road information [1,2] and play an important role in the construction of surveying and mapping geographic information. Fine, accurate, efficient, and fast automatic road edge extraction technology promotes the development of digital cities, emergency commands, road maintenance, and 3-D maps, and allows for the rapid development of new technologies, such as intelligent navigation, self-driving vehicles, and intelligent transportation.…”

Section: Introductionmentioning

confidence: 99%

“…The MLS can rapidly, continuously, and reliably capture high-accuracy 3-D spatially referenced and other information (e.g., intensity, color) from road infrastructure and surrounding road corridor environments [9]. A number of previous studies have focused on extraction and recognition of various objects based on MLS data, such as roads [1,2,9], road markings [13,14,15,16,17], and others [10,11,18,19,20,21]. At present, the technology to extract road edges [22,23], which is key to acquiring road geometry information and road objects [9], cannot simultaneously satisfy both structural (different road curb types) and non-structural road types (such as that of a grass-soil road).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data

Yang

Liu

Jiang

et al. 2019

Sensors

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Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.

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Road Lane Line Detection Based on ROI Using Hough Transform Algorithm

Syed

Kumar²

2022

Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

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Road surface and its center line and boundary lines detection using terrestrial Lidar data

Cited by 10 publications

References 23 publications

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data

Road Lane Line Detection Based on ROI Using Hough Transform Algorithm

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