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Vehicle autonomous localization in local area of coal mine tunnel based on vision sensors and ultrasonic sensors.
- Source :
-
PLoS ONE . 1/31/2017, Vol. 12 Issue 1, p1-31. 31p. - Publication Year :
- 2017
-
Abstract
- This paper presents a vehicle autonomous localization method in local area of coal mine tunnel based on vision sensors and ultrasonic sensors. Barcode tags are deployed in pairs on both sides of the tunnel walls at certain intervals as artificial landmarks. The barcode coding is designed based on UPC-A code. The global coordinates of the upper left inner corner point of the feature frame of each barcode tag deployed in the tunnel are uniquely represented by the barcode. Two on-board vision sensors are used to recognize each pair of barcode tags on both sides of the tunnel walls. The distance between the upper left inner corner point of the feature frame of each barcode tag and the vehicle center point can be determined by using a visual distance projection model. The on-board ultrasonic sensors are used to measure the distance from the vehicle center point to the left side of the tunnel walls. Once the spatial geometric relationship between the barcode tags and the vehicle center point is established, the 3D coordinates of the vehicle center point in the tunnel’s global coordinate system can be calculated. Experiments on a straight corridor and an underground tunnel have shown that the proposed vehicle autonomous localization method is not only able to quickly recognize the barcode tags affixed to the tunnel walls, but also has relatively small average localization errors in the vehicle center point’s plane and vertical coordinates to meet autonomous unmanned vehicle positioning requirements in local area of coal mine tunnel. [ABSTRACT FROM AUTHOR]
- Subjects :
- *VEHICLE detectors
*COAL mining
*TUNNELS
*IMAGE sensors
*ULTRASONIC equipment
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 121074204
- Full Text :
- https://doi.org/10.1371/journal.pone.0171012