A Fast Edge Extraction Method for Mobile Lidar Point Clouds

2017 ◽  
Vol 14 (8) ◽  
pp. 1288-1292 ◽  
Author(s):  
Shaobo Xia ◽  
Ruisheng Wang
Author(s):  
R. Honma ◽  
H. Date ◽  
S. Kanai

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.


2021 ◽  
Vol 15 (3) ◽  
pp. 258-267
Author(s):  
Hiroki Matsumoto ◽  
◽  
Yuma Mori ◽  
Hiroshi Masuda

Mobile mapping systems can capture point clouds and digital images of roadside objects. Such data are useful for maintenance, asset management, and 3D map creation. In this paper, we discuss methods for extracting guardrails that separate roadways and walkways. Since there are various shape patterns for guardrails in Japan, flexible methods are required for extracting them. We propose a new extraction method based on point processing and a convolutional neural network (CNN). In our method, point clouds and images are segmented into small fragments, and their features are extracted using CNNs for images and point clouds. Then, features from images and point clouds are combined and investigated using whether they are guardrails or not. Based on our experiments, our method could extract guardrails from point clouds with a high success rate.


2010 ◽  
Vol 39 (4) ◽  
pp. 759-763
Author(s):  
陈亮 CHEN Liang ◽  
郭雷 GUO Lei

2014 ◽  
Vol 687-691 ◽  
pp. 3765-3768
Author(s):  
Nan Wang

A new edge extraction method was put forward based on the SUSAN operator, according to the problems of poor anti-noise ability and edge detection incomplete of the conventional differential detection operator. The circular template and the center of the circle (template nuclear) were used in this method, the numbers of pixels was calculated through the comparison pixels value of template with the other points of pixels in the template circle, and then compared with the threshold, so as to the edge of images was extracted. The results showed that this method had high precision, and could be able to fully extract the edge of images. It is an effective method of extracting the edge of images.


2008 ◽  
Vol 16 (1) ◽  
Author(s):  
A. Walczak ◽  
L. Puzio

AbstractThe novel two-dimensional (2D) wavelet with anisotropic property and application of it has been presented. Wavelet is constructed in the polar coordinate system to obtain anisotropic properties. A novel edge detection method has been developed with the aid of this wavelet. This method detects gradient jump and than follows along this jump. In this way the number of calculation for edge localization is reduced. Moreover, the presented method is able to detect all edges in an image in multi-scale together with its spatial orientation. Proposed wavelet as well as edge extraction method seems to be new way to edge detection for an image.


2016 ◽  
Vol 55 (9) ◽  
pp. 094104 ◽  
Author(s):  
Wei Yin ◽  
Xiaosheng Cheng ◽  
Haihua Cui ◽  
Dawei Li ◽  
Lei Zhou

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Lei Wang ◽  
Jingyu Li ◽  
Chuang Jiang ◽  
Jinzhong Huang

Author(s):  
Masayuki EGUCHI ◽  
Akira KAWAMURA ◽  
Kazuya TOMIYAMA ◽  
Shigeki TAKAHASHI ◽  
Shinichiro OMACHI

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