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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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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1725335140720181248 |