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A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks
Haitao Liu1, Wenbo Pan1, Yunqing Hu1
1CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China.
Sensors (Basel, Switzerland)
|August 26, 2022
Summary
This study introduces a new method for detecting and tracking obstacles in open-pit mines using Lidar and millimeter-wave radar. The approach enhances perception system reliability in dusty environments and unpaved roads.
Area of Science:
- Robotics and Autonomous Systems
- Environmental Sensing
- Mining Engineering
Background:
- Environmental perception in open-pit mines faces challenges like unpaved roads, dust, and detecting small, irregular obstacles.
- Existing methods struggle with stability and accuracy in these demanding conditions.
Purpose of the Study:
- To develop a robust multi-target detection and tracking method for open-pit mine transportation.
- To enhance the reliability and accuracy of perception systems in challenging mining environments.
Main Methods:
- Fusion of Lidar and millimeter-wave radar data.
- A secondary segmentation algorithm tailored for open-pit mine scenarios.
- An adaptive heterogeneous multi-source fusion strategy for dust filtering.
Main Results:
- Stable detection of 30-40 cm obstacles at 60 meters in front of mining trucks.
- Effective filtering of false alarms caused by dense dust.
- Improved detection and tracking of various targets in dusty conditions.
Conclusions:
- The proposed fusion method significantly enhances environmental perception in open-pit mines.
- The system demonstrates reliability and accuracy in detecting small obstacles and managing dust interference.
