Research on Positioning and Tracking Method of Intelligent Mine Car in Underground Mine Based on YOLOv5 Algorithm and Laser Sensor Fusion

Linxin Zhang, Xiaoquan Li (), Yunjie Sun, Junhong Liu and Yonghe Xu
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Linxin Zhang: School of Resources Environment and Materials, Guangxi University, 100 University East Road, Nanning 530004, China
Xiaoquan Li: School of Resources Environment and Materials, Guangxi University, 100 University East Road, Nanning 530004, China
Yunjie Sun: School of Resources Environment and Materials, Guangxi University, 100 University East Road, Nanning 530004, China
Junhong Liu: School of Resources Environment and Materials, Guangxi University, 100 University East Road, Nanning 530004, China
Yonghe Xu: School of Resources Environment and Materials, Guangxi University, 100 University East Road, Nanning 530004, China

Sustainability, 2025, vol. 17, issue 2, 1-24

Abstract: Precise positioning has become a key technology in the intelligent development of underground mines. To improve the positioning accuracy of mining vehicles, this paper proposes an intelligent underground mining vehicle positioning and tracking method based on the fusion of the YOLOv5 and laser sensor technology. The system utilizes a camera and the YOLOv5 algorithm for real-time identification and precise tracking of mining vehicles, while the laser sensor is used to accurately measure the straight-line distance between the vehicle and the positioning device. By combining the strengths of both vision and laser sensors, the system can efficiently identify mining vehicles in complex environments and accurately calculate their position using geometric principles based on laser distance measurements. Experimental results show that the YOLOv5 algorithm can efficiently identify and track mining vehicles in real time. When integrated with the laser sensor’s distance measurement, the system achieves high-precision positioning, with horizontal and vertical positioning errors of 1.66 cm and 1.96 cm, respectively, achieving centimeter-level accuracy overall. This system significantly improves the accuracy and real-time performance of mining vehicle positioning, effectively reducing operational errors and safety risks, providing essential technical support for the intelligent development of underground mining transportation systems.

Keywords: intelligent mining vehicle; YOLO algorithm; multi-sensor fusion; underground mine positioning; mine intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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