Next Article in Journal
A Polygon and Point-Based Approach to Matching Geospatial Features
Next Article in Special Issue
Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads
Previous Article in Journal
Trends and Opportunities of BIM-GIS Integration in the Architecture, Engineering and Construction Industry: A Review from a Spatio-Temporal Statistical Perspective
Previous Article in Special Issue
Accuracy Improvement of DGPS for Low-Cost Single-Frequency Receiver Using Modified Flächen Korrektur Parameter Correction
Article Menu

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(12), 398; doi:10.3390/ijgi6120398

Development of a Change Detection Method with Low-Performance Point Cloud Data for Updating Three-Dimensional Road Maps

Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo Bunkyo, Tokyo 113-8656, Japan
*
Author to whom correspondence should be addressed.
Received: 24 October 2017 / Revised: 17 November 2017 / Accepted: 1 December 2017 / Published: 4 December 2017
(This article belongs to the Special Issue Mapping for Autonomous Vehicles)
View Full-Text   |   Download PDF [6899 KB, uploaded 4 December 2017]   |  

Abstract

Three-dimensional (3D) road maps have garnered significant attention recently because of applications such as autonomous driving. For 3D road maps to remain accurate and up-to-date, an appropriate updating method is crucial. However, there are currently no updating methods with both satisfactorily high frequency and accuracy. An effective strategy would be to frequently acquire point clouds from regular vehicles, and then take detailed measurements only where necessary. However, there are three challenges when using data from regular vehicles. First, the accuracy and density of the points are comparatively low. Second, the measurement ranges vary for different measurements. Third, tentative changes such as pedestrians must be discriminated from real changes. The method proposed in this paper consists of registration and change detection methods. We first prepare the synthetic data obtained from regular vehicles using mobile mapping system data as a base reference. We then apply our proposed change detection method, in which the occupancy grid method is integrated with Dempster–Shafer theory to deal with occlusions and tentative changes. The results show that the proposed method can detect road environment changes, and it is easy to find changed parts through visualization. The work contributes towards sustainable updates and applications of 3D road maps. View Full-Text
Keywords: three-dimensional road map; change detection; point cloud data; mobile mapping system; occupancy grid method; Dempster–Shafer theory three-dimensional road map; change detection; point cloud data; mobile mapping system; occupancy grid method; Dempster–Shafer theory
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Fuse, T.; Yokozawa, N. Development of a Change Detection Method with Low-Performance Point Cloud Data for Updating Three-Dimensional Road Maps. ISPRS Int. J. Geo-Inf. 2017, 6, 398.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top