An Automated Rail Extraction Framework for Low-Density LiDAR Data Without Sensor Configuration Information-Reference-Cited by-同舟云学术

An Automated Rail Extraction Framework for Low-Density LiDAR Data Without Sensor Configuration Information

Published:2022-07-01 Issue:13 Volume:22 Page:13234-13243
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Ren Yihao¹^ORCID,Ai Chengbo²^ORCID,Lu Pan¹^ORCID,Dai Zhenyu³,Wang Hao⁴

Affiliation:

1. Department of Transportation, Logistics, and Finance, Upper Great Plains Transportation Institute, North Dakota State University (NDSU), Fargo, ND, USA

2. Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, MA, USA

3. Department of Physics, University of Houston, Houston, TX, USA

4. Department of Civil and Environmental Engineerin, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

Funder

North Dakota State University and the Mountain-Plains Consortium, a University Transportation Center funded by the U.S. Department of Transportation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9812846/09785432.pdf?arnumber=9785432

Reference28 articles.

1. Automated Extraction of 3-D Railway Tracks from Mobile Laser Scanning Point Clouds

2. A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

3. Rail Track Detection and Modelling in Mobile Laser Scanner Data

4. Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data

5. Application of Template Matching for Improving Classification of Urban Railroad Point Clouds

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