NDLI: Turning mobile laser scanning points into 2D/3D on-road object models: Current status

Content Provider	IEEE Xplore Digital Library
Author	Zongliang Zhang Ming Cheng Xinqu Chen Menglan Zhou Yifei Chen Li, J. Hongshan Nie
Copyright Year	2015
Description	Author affiliation: Hunan Intell. Things Technol. Co., Ltd., Changsha, China (Hongshan Nie) \|\| Dept. of Geogr. & Environ. Manage., Univ. of Waterloo, Waterloo, ON, Canada (Xinqu Chen; Menglan Zhou; Yifei Chen) \|\| Fujian Key Lab. of Sensing & Comput. for Smart City, Xiamen Univ., Xiamen, China (Zongliang Zhang; Ming Cheng; Li, J.)
Abstract	Traditional road surveying methods rely largely on in-situ measurements, which are time consuming and labor intensive. Recent Mobile Laser Scanning (MLS) techniques enable collection of road data at a normal driving speed. However, extracting required information from collected MLS data remains a challenging task. This paper focuses on examining the current status of automated on-road object extraction techniques from 3D MLS points over the last five years. Several kinds of on-road objects are included in this paper: curbs and road surfaces, road markings, pavement cracks, as well as manhole and sewer well covers. We evaluate the extraction techniques according to their method design, degree of automation, precision, and computational efficiency. Given the large volume of MLS data, to date most MLS object extraction techniques are aiming to improve their precision and efficiency.
Starting Page	3524
Ending Page	3527
File Size	577257
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326581
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Roads Three-dimensional displays Mobile communication Data mining Remote sensing Feature extraction Laser radar road Mobile laser scanning (MLS) 3D point clouds object extraction
Content Type	Text
Resource Type	Article

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