Content area
Proposes a LiDAR-assisted two-stage ghost noise automatic labeling method for 4D mmWave radar data, combining distance threshold filtering and density-based clustering analysis (DBSCAN), which demonstrates superior performance compared to single-method approaches. Designs a complete automated labeling workflow tailored for underground mining environments, significantly reducing the cost and complexity of manual labeling while addressing current data annotation bottlenecks in research.
Validates the proposed method’s efficiency and robustness in ghost noise detection across three typical underground mining scenarios (straight tunnels, straight tunnels with side tunnels, and cross-tunnel turns), providing a practical solution for optimizing radar data quality in complex confined environments. Lays an important foundation for the application of 4D mmWave radar in underground mining environments and provides new technical means for studying ghost noise labeling issues, with potential applications in similar industrial settings. 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.
Details
Confined spaces;
Mines;
Underground mining;
Accuracy;
Labels;
Datasets;
Tunnels;
Labeling;
Radar;
Radar data;
Lidar;
Synchronization;
Signal processing;
Workflow;
Mapping;
Automation;
Annotations;
Mining;
Time synchronization;
Density;
Cluster analysis;
Underground mines;
Ghosts;
Clustering;
Sensors;
Target detection;
Autonomous navigation;
Methods;
Complexity;
Millimeter waves;
Robustness (mathematics);
Filtration
; Yang, Jing 4 1 School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China; [email protected] (H.L.); [email protected] (G.C.), Department of Mine Surveying and Geodesy, TU Bergakademie Freiberg, 09599 Freiberg, Germany; [email protected]
2 School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China; [email protected] (H.L.); [email protected] (G.C.)
3 Department of Mine Surveying and Geodesy, TU Bergakademie Freiberg, 09599 Freiberg, Germany; [email protected]
4 State Key Laboratory of Earthquake Dynamics and Forecasting, Institute of Geology, China Earthquake Administration, Beijing 100029, China; [email protected]