A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks

Abstract

There exist many difficulties in environmental perception in transportation at open-pit mines, such as unpaved roads, dusty environments, and high requirements for the detection and tracking stability of small irregular obstacles. In order to solve the above problems, a new multi-target detection and tracking method is proposed based on the fusion of Lidar and millimeter-wave radar. It advances a secondary segmentation algorithm suitable for open-pit mine production scenarios to improve the detection distance and accuracy of small irregular obstacles on unpaved roads. In addition, the paper also proposes an adaptive heterogeneous multi-source fusion strategy of filtering dust, which can significantly improve the detection and tracking ability of the perception system for various targets in the dust environment by adaptively adjusting the confidence of the output target. Finally, the test results in the open-pit mine show that the method can stably detect obstacles with a size of 30–40 cm at 60 m in front of the mining truck, and effectively filter out false alarms of concentration dust, which proves the reliability of the method.