Next Article in Journal
Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery
Next Article in Special Issue
Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories
Previous Article in Journal
Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier
Previous Article in Special Issue
Panoramic Image and Three-Axis Laser Scanner Integrated Approach for Indoor 3D Mapping
Review

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

1
Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue, Waterloo, ON N2L 3G1, Canada
2
Fujian Key Laboratory of Sensing and Computing, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen 361005, China
3
Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
4
Department of Geomatics Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
5
Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
*
Author to whom correspondence should be addressed.
The first two authors contributed equally to this paper.
Remote Sens. 2018, 10(10), 1531; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101531
Received: 5 July 2018 / Revised: 20 September 2018 / Accepted: 22 September 2018 / Published: 24 September 2018
(This article belongs to the Special Issue Mobile Laser Scanning)
The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services. View Full-Text
Keywords: mobile laser scanning (MLS); point cloud; road surface; road marking; driving line; road crack; traffic sign; street light; tree; power line; deep learning mobile laser scanning (MLS); point cloud; road surface; road marking; driving line; road crack; traffic sign; street light; tree; power line; deep learning
Show Figures

Graphical abstract

MDPI and ACS Style

Ma, L.; Li, Y.; Li, J.; Wang, C.; Wang, R.; Chapman, M.A. Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review. Remote Sens. 2018, 10, 1531. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101531

AMA Style

Ma L, Li Y, Li J, Wang C, Wang R, Chapman MA. Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review. Remote Sensing. 2018; 10(10):1531. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101531

Chicago/Turabian Style

Ma, Lingfei, Ying Li, Jonathan Li, Cheng Wang, Ruisheng Wang, and Michael A. Chapman 2018. "Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review" Remote Sensing 10, no. 10: 1531. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101531

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop