A Fast and Robust Algorithm for Road Edges Extraction From Lidar Data

Abstract

ABSTRACT:Fast mapping of roads plays an important role in many geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance. How to extract various road edges fast and robustly is a challenging task. In this paper, we present a fast and robust algorithm for the automatic road edges extraction from terrestrial mobile LiDAR data. The algorithm is based on a key observation: most roads around edges have difference in elevation and road edges with pavement are seen in two different planes. In our algorithm, we firstly extract a rough plane based on RANSAC algorithm, and then multiple refined planes which only contains pavement are extracted from the rough plane. The road edges are extracted based on these refined planes. In practice, there is a serious problem that the rough and refined planes usually extracted badly due to rough roads and different density of point cloud. To eliminate the influence of rough roads, the technology which is similar with the difference of DSM (digital surface model) and DTM (digital terrain model) is used, and we also propose a method which adjust the point clouds to a similar density to eliminate the influence of different density. Experiments show the validities of the proposed method with multiple datasets (e.g. urban road, highway, and some rural road). We use the same parameters through the experiments and our algorithm can achieve real-time processing speeds.

A Boundary-Enhanced Supervoxel Method for Extraction of Road Edges in MLS Point Clouds

“…where × denotes the cross product operation. Then we use the spatial judgement to cluster points by setting thresholds (Qiu et al, 2016):…”

Automatic Road Structure Detection and Vectorization Using MLS Point Clouds

“…This algorithm performs well on Road boundaries in straight shape parallel with the vehicle trajectory, but cannot do well with the situations where boundary and trajectory are not really parallel. Qiu et al (2016) extracted planes using RANSAC from coarse to fine, and road boundaries are considered as edges of the fined-tuned planes. Road width and slope continuity of adjacent points are employed to refine extracted edge points (Qiu et al, 2016).…”

“…Qiu et al (2016) extracted planes using RANSAC from coarse to fine, and road boundaries are considered as edges of the fined-tuned planes. Road width and slope continuity of adjacent points are employed to refine extracted edge points (Qiu et al, 2016). The width and roughness of roads vary a lot in real-world situations, therefore using this methodology to extract boundary automatically is a tough task.…”

An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning

“…Pastucha [16] used a density-based method to extract the support structure of the cables wire. However, due to the diversification of density interfered statistical methods, the authors ensured uniform density using resampling method [21,28]. The fusion of multi-source data will add more new features to point clouds and bring new detection methods.…”

“…Telke et al [29] improved signal processing for video-based measurement methods. Tang et al [30] added new information of the infrared spectrum to the point clouds, Qiu et al [21] added the information of the digital terrain models (DTM) to the point clouds.…”