Rapid Extraction of Urban Road Guardrails From Mobile LiDAR Point Clouds

Abstract

Mobile Laser Scanning (MLS) systems provide highly dense 3D point clouds that enable the acquisition of accurate traffic facilities information for intelligent transportation system. Road guardrails with safety features that can separate traffic and define moving spaces for pedestrians and vehicles face challenges such as diverse guardrail types and continuous slopes in point clouds data. This paper proposes a novel approach for rapidly extracting urban road guardrails from MLS point clouds, combining a proposed multi-level filtering with a modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering, and adapting for most types of guardrails and rough slope roads. We develop a multi-level filter to detect the road surface and remove the undesirable points. Through a proposed modified DBSCAN clustering, the guardrails are extracted after a four-step screening, which includes the limits based on the number of points, the fitting error, the bounding box size and the average reflection intensity for each cluster. The proposed method achieves high precisions of 97.2% and 96.4% respectively for the lane-separating guardrails and the anti-fall guardrails on the dataset. Extensive experiments with test dataset captured by a RIEGL VMX-450 MLS, show that our method outperforms the state-of-the-art method to extract 3D guardrails from point clouds.