Combined Lane Mapping Using a Mobile Mapping System

High-definition mapping of 3D lane lines has been widely needed for the highway documentation and intelligent navigation of autonomous systems. A mobile mapping system (MMS) captures both accurate 3D LiDAR point clouds and high-resolution images of lane markings at highway driving speeds, providing...

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Main Authors: Rui Wan, Yuchun Huang, Rongchang Xie, Ping Ma
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/11/3/305
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spelling doaj-9f98d32c4fa14eb2b3ef4d0911ab0cc52020-11-25T00:28:38ZengMDPI AGRemote Sensing2072-42922019-02-0111330510.3390/rs11030305rs11030305Combined Lane Mapping Using a Mobile Mapping SystemRui Wan0Yuchun Huang1Rongchang Xie2Ping Ma3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaCenter for Data Science, Peking University, Beijing 100871, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaHigh-definition mapping of 3D lane lines has been widely needed for the highway documentation and intelligent navigation of autonomous systems. A mobile mapping system (MMS) captures both accurate 3D LiDAR point clouds and high-resolution images of lane markings at highway driving speeds, providing an abundant data source for combined lane mapping. This paper aims to map lanes with an MMS. The main contributions of this paper include the following: (1) an intensity correction method was introduced to eliminate the reflectivity inconsistency of road-surface LiDAR points; (2) a self-adaptive thresholding method was developed to extract lane markings from their complicated surroundings; and (3) a LiDAR-guided textural saliency analysis of MMS images was proposed to improve the robustness of lane mapping. The proposed method was tested with a dataset acquired in Wuhan, Hubei, China, which contained straight roads, curved roads, and a roundabout with various pavement markings and a complex roadside environment. The experimental results achieved a recall of 96.4%, a precision of 97.6%, and an F-score of 97.0%, demonstrating that the proposed method has strong mapping ability for various urban roads.https://www.mdpi.com/2072-4292/11/3/305mobile mapping system(MMS)point cloudlane markinglane mapping
collection DOAJ
language English
format Article
sources DOAJ
author Rui Wan
Yuchun Huang
Rongchang Xie
Ping Ma
spellingShingle Rui Wan
Yuchun Huang
Rongchang Xie
Ping Ma
Combined Lane Mapping Using a Mobile Mapping System
Remote Sensing
mobile mapping system(MMS)
point cloud
lane marking
lane mapping
author_facet Rui Wan
Yuchun Huang
Rongchang Xie
Ping Ma
author_sort Rui Wan
title Combined Lane Mapping Using a Mobile Mapping System
title_short Combined Lane Mapping Using a Mobile Mapping System
title_full Combined Lane Mapping Using a Mobile Mapping System
title_fullStr Combined Lane Mapping Using a Mobile Mapping System
title_full_unstemmed Combined Lane Mapping Using a Mobile Mapping System
title_sort combined lane mapping using a mobile mapping system
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-02-01
description High-definition mapping of 3D lane lines has been widely needed for the highway documentation and intelligent navigation of autonomous systems. A mobile mapping system (MMS) captures both accurate 3D LiDAR point clouds and high-resolution images of lane markings at highway driving speeds, providing an abundant data source for combined lane mapping. This paper aims to map lanes with an MMS. The main contributions of this paper include the following: (1) an intensity correction method was introduced to eliminate the reflectivity inconsistency of road-surface LiDAR points; (2) a self-adaptive thresholding method was developed to extract lane markings from their complicated surroundings; and (3) a LiDAR-guided textural saliency analysis of MMS images was proposed to improve the robustness of lane mapping. The proposed method was tested with a dataset acquired in Wuhan, Hubei, China, which contained straight roads, curved roads, and a roundabout with various pavement markings and a complex roadside environment. The experimental results achieved a recall of 96.4%, a precision of 97.6%, and an F-score of 97.0%, demonstrating that the proposed method has strong mapping ability for various urban roads.
topic mobile mapping system(MMS)
point cloud
lane marking
lane mapping
url https://www.mdpi.com/2072-4292/11/3/305
work_keys_str_mv AT ruiwan combinedlanemappingusingamobilemappingsystem
AT yuchunhuang combinedlanemappingusingamobilemappingsystem
AT rongchangxie combinedlanemappingusingamobilemappingsystem
AT pingma combinedlanemappingusingamobilemappingsystem
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