Extraction of road boundary from MLS data using laser scanner ground trajectory

Abstract

Various means of extracting road boundary from mobile laser scanning data based on vehicle trajectories have been investigated. Independent of positioning and navigation data, this study estimated the scanner ground track from the spatial distribution of the point cloud as an indicator of road location. We defined a typical edge block consisting of multiple continuous upward fluctuating points by abrupt changes in elevation, upward slope, and road horizontal slope. Subsequently, such edge blocks were searched for on both sides of the estimated track. A pseudo-mileage spacing map was constructed to reflect the variation in spacing between the track and edge blocks over distance, within which road boundary points were detected using a simple linear tracking model. Experimental results demonstrate that the ground trajectory of the extracted scanner forms a smooth and continuous string just on the road; this can serve as the basis for defining edge block and road boundary tracking algorithms. The defined edge block has been experimentally verified as highly accurate and strongly noise resistant, while the boundary tracking algorithm is simple, fast, and independent of the road boundary model used. The correct detection rate of the road boundary in two experimental data is more than 99.2%.

Road Curbs Extraction from Mobile Laser Scanning Point Clouds with Multidimensional Rotation‐Invariant Version of the Local Binary Pattern Features

“…According to the existing research results and engineering experience, it is an effective and efficient way to process MLS point clouds in blocks or segments (Gao et al, 2022; Safaie et al, 2021; Sui et al, 2021; Yang et al, 2017; Zai et al, 2018). Both the 3D point clouds and the vehicle trajectory data in the same coordinate system can be provided by the MLS system.…”

“…Next, indirect features such as histograms (Miraliakbari et al, 2015), saliency map (Wang et al, 2015) and voxel‐based energy functions (Xu et al, 2017) are exploited. Subsequently, the feature vector or feature description operator fused with multiple features (such as elevation, slope, normal, spatial distance, intensity and collinearity) to extract curbs has been studied by many scholars (Luo et al, 2017; Ma et al, 2019; Sui et al, 2021; Yang et al, 2017; Zai et al, 2018; Zhao et al, 2021). In addition, the snake model has also been directly used for road curb detection in point clouds (Fang et al, 2020).…”