Research on Target-detection and Tracking of Intelligent Mine Trucks in Open-pit Mines

Abstract

To ensure the safety of mine trucks on unpaved roads and in dusty environments, it's necessary to improve the efficiency and robustness of targets detection and tracking. This paper proposes a multi-target detection and tracking algorithm based on the fusion of light detection and ranging (LiDAR) and millimeter-wave radar. The algorithm includes point cloud feature extraction module, target tracking module and heterogeneous sensor fusion module. Among them, feature extraction module uses point cloud gradient and distance feature extraction methods to solve the point cloud segmentation problem of small and irregular-shaped obstacles in changing scenes; target tracking module constructs track information for multi-target tracking, which improves the stability of target tracking; an asynchronous fusion strategy of multi-source sensors is designed for heterogeneous sensor fusion module, which overcomes the problem of heterogeneous sensor fusion, and improves the detection and tracking capabilities of small and various targets in unpaved road and dusty environment of open-pit mines. Finally, verification experiment is carried out on the mine trucks based on ROS(robot operating system) framework and the results show that it can accurately detect mine trucks in the range of 200 m and objects of 30 cm in the range of 50 m, which demonstrate the stability, accuracy and reliability of the method.

Keywords

• open pit
• point cloud segmentation
• multi-source information fusion
• environmental perception
• target detection