Automatic Ghost Noise Labeling for 4D mmWave Radar Data in Underground Mine Environments Using LiDAR as Reference
2025
Hu Liu | Zhenghua Zhang | Guoliang Chen | Jörg Benndorf | Jing Yang
In underground mining environments, 4D mmWave radar performance is severely constrained by ghost noise issues resulting from multipath reflections, metal structure interference, and complex terrain, creating significant challenges for target detection, mapping, and autonomous navigation tasks. Existing research lacks efficient automated methods and technical workflows for ghost point labeling in these scenarios. This paper presents a LiDAR-assisted two-stage ghost noise automatic labeling method. The technical workflow first achieves precise mapping between radar and LiDAR point clouds through multi-sensor spatiotemporal alignment (time synchronization and spatial registration) and then labels ghost points using a two-stage strategy that combines distance threshold filtering with density-based clustering analysis (DBSCAN). Experiments covering three typical underground mining scenarios (straight tunnels, straight tunnels with side tunnels, and cross-tunnel turns) demonstrate that the proposed method significantly outperforms single distance threshold or clustering methods in terms of precision (95.15%, 98.81%, and 98.85%, respectively), recall (97.44%, 94.68%, and 98.03%, respectively, slightly lower than distance threshold methods in straight tunnels and cross-tunnel turns), and F1 Score (95.48%, 96.70%, and 98.01%, respectively). The method exhibits efficient ghost noise detection capability and robustness in underground mining environments, providing a practical solution for optimizing radar data quality in complex confined scenarios, with potential for application in similar industrial settings.
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