Fused Raised Pavement Marker Detection Using 2D-Lidar and Mono Camera
In this paper, the authors describe a raised pavement markers detection algorithm using a monocular camera and a 2D lidar that supports automated driving in urban and highways scenes. For each sensor, the authors present a detection method based on its sensing properties and range. The results of these approaches are tracked with a multi Kalman filter and are represented as mixtures of Gaussians. Afterwards, the authors use a Random Sample Consensus (RANSAC) spline fitting algorithm for a robust estimation of the road boundary. To improve the precision of the applied RANSAC the authors use the detected painted lane markings. Eventually, the authors fuse the tracked features with the lane markings on the basis of a product approximation method for Gaussian Mixtures. Experimental results show the reliability and the real-time capability of the authors' approach based on the data they collected in California and Nevada.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9781467365956
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Supplemental Notes:
- Abstract reprinted with permission of IEEE.
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Corporate Authors:
Institute of Electrical and Electronics Engineers (IEEE)
3 Park Avenue, 17th Floor
New York, NY United States 10016-5997 -
Authors:
- Nguyen, Tran Tuan
- Spehr, Jens
- Lin, Tsung-Han Hank
- Lipinski, Daniel
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Conference:
- 18th International IEEE Conference on Intelligent Transportation Systems (ITSC)
- Location: Canary Islands , Spain
- Date: 2015-9-15 to 2015-9-18
- Publication Date: 2015
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 2346-2351
- Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)
Subject/Index Terms
- TRT Terms: Algorithms; Cameras; Detection and identification; Intelligent vehicles; Kalman filtering; Laser radar; Raised road markings
- Uncontrolled Terms: Gaussian processes
- Geographic Terms: California; Nevada
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01599785
- Record Type: Publication
- ISBN: 9781467365956
- Files: TRIS
- Created Date: May 22 2016 6:35PM