NDLI: A monocular vision localization method based on unmanned underground mining vehicle using barcode

Content Provider	IEEE Xplore Digital Library
Author	Guanpu Shen Xiaojuan Ban Zheng Chang Shansong Shi Shuxin Chen
Copyright Year	2010
Description	Author affiliation: Civil Environment Engineering School, University of Science and Technology Beijing, 100083, China (Shuxin Chen) \|\| Information Engineering School, University of Science and Technology Beijing, 100083, China (Guanpu Shen; Xiaojuan Ban; Zheng Chang; Shansong Shi)
Abstract	A method to compute the lean stance of the camera is proposed, which is based on the parallel perspective mapping model of the camera. This method improved the common monocular vision-based localization method and can be used in the operating system of unmanned underground mining vehicle to detect the travelling status of vehicle in real-time, fit the bad underground road condition, and reduce the distance detection error which occurred when the camera is lean to the road. This method computes the lean angle of the camera according to the high of landmark and the interior parameters of the camera, so we can detect the distance between the vehicle and the landmark and reduce the error which occurred if the optical axis. Experiments show that this method can faster detect the lean angle which is one of the camera exterior parameters. It is of high precision and fit real-time processing. This method is easy and effective, can reduce the detection error and is suitable for the requirement of underground mining work.
Starting Page	1071
Ending Page	1075
File Size	660084
Page Count	5
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554898
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cameras Mathematical model Calibration Optical imaging Distance measurement Roads lean stance machine vision monocular localization parallel perspective
Content Type	Text
Resource Type	Article

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