Paper Info
Title
Infrared LEDs-Based Pose Estimation With Underground Camera Model for Boom-Type Roadheader in Coal Mining.
Abstract
Accurate and reliable pose estimation of a boom-type roadheader is of great importance in order to maintain the efficiency of automatic coal mining. The stability and accuracy of conventional measurement methods are difficult to be guaranteed on account of vibration noise, magnetic disturbance, electrostatic interference, and other factors in an underground environment. In this paper, a vision-based non-contact measurement method for cutting-head pose estimation is presented, which deploy a 16-point infrared LED target on the boom-type roadheader to tackle the low-illumination, high-dust, and complicated background. By establishing a monocular vision measurement system, the cutting-head pose is estimated through processing the LED target images obtained from an explosion-proof industrial camera mounted on the roadheader. After analyzing the measurement mechanism, an underground camera model based on the equivalent focal length is built to eliminate refraction errors caused by the two-layer glasses for explosion-proof and dust removal. Then, the pose estimation processes, including infrared LEDs feature point extraction, spot center location, and an improved P4P method based on dual quaternions, are carried out. The influence factors of cutting-head pose estimation accuracy are further studied by modeling, and the error distribution of the main parameters is investigated and evaluated. The numerical simulation and experimental evaluation are designed to verify the performance of the proposed method. The results show that the pose estimation error is in line with the numerical prediction, achieving the requirements of cutting-head pose estimation in underground roadway construction in the coal mine.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2904097
IEEE ACCESS
Keywords
Field
DocType
Vision measurement,infrared LEDs,pose estimation,underground camera model,total accuracy error,boom-type roadheader
Monocular vision,Computer vision,Dual quaternion,System of measurement,Computer simulation,Computer science,Coal mining,Pose,Focal length,Artificial intelligence,Distributed computing,Roadheader
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wenjuan Yang102.70
Xuhui Zhang254.31
Hongwei Ma33610.57
Guang-Ming Zhang452.00