Abstract
Abstract. Efficient road edge extraction from point clouds acquired by Mobile Laser Scanning (MLS) is an important task because the road edge is one of the main elements of high definition maps. In this paper, we present a scanline-based road edge extraction method using a bend angle of scanlines from MLS point clouds. Scanline-based methods have advantages in that computational cost is low, it is easy to extract accurate road edges, and they are independent of driving speed of MLS compared to methods using unorganized points. In contrast, there are some problems with these methods where the extraction accuracy becomes low at curb cuts and intersections. The extraction accuracy becomes low caused by the scanning noise and small occlusion from weeds and fallen leaves. In addition, some parameters should be adjusted according to the mounting angle of the laser scanner on the vehicle. Therefore, we present a scanline-based road edge extraction method which can solve these problems. First, the points of the scanline are projected to a plane in order to reduce the influence of the mounting angle of the laser scanner on the vehicle. Next, the bend angle of each point is calculated by using filtered point clouds which are not vulnerable to small occlusions around the curb such as weeds. Then, points with a local maximum of bend angle and close to trajectories are extracted as seed points. Finally, road edges are generated by tracking based on bend angle of scanlines and smoothness of road edges from the seed points. In the experiments, our proposed methods achieved a completeness of over 95.3%, a correctness of over 95.0%, a quality of over 90.7%, and RMS difference less than 18.7 mm in total.
Highlights
Point clouds acquired by Mobile Laser Scanning (MLS) have been applied to efficient road asset management and the improvement in accuracy and generation cost of high definition maps
As the unorganized point-based methods, Qiu et al (2016) extracts candidate points of road edges from multiple planes extracted by RANSAC algorithm, and extracted road edge points from candidate points based on the stability and continuity of the road width
We proposed an accurate extraction method of the road edges from point clouds using the bend angle of the scanline acquired by MLS
Summary
Point clouds acquired by Mobile Laser Scanning (MLS) have been applied to efficient road asset management and the improvement in accuracy and generation cost of high definition maps. Many methods for automatic extraction of road edges have been proposed (e.g. Qiu et al, 2016, Hervieu et al, 2013, Zai et al, 2018, Yang et al, 2013, and Ishikawa et al, 2018). Road edges are acquired based on curb extraction by evaluation of local points distribution. These methods can be classified into unorganized points-based methods and scanline-based methods by evaluation processes of local points. Hervieu et al (2013) proposed a method to detect curb points using Kalman filter inspired method from roadside points extracted by evaluation of the difference of normal vector and estimated plane direction using RANSAC.
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