关键词: 无人驾驶矿车, 激光雷达, 三维点云, 数据增强, 物理仿真, 目标检测

Abstract: Current 3D object detection models predominantly utilize data-driven deep learning techniques, in which dataset quality plays a pivotal role in model performance. To mitigate the scarcity of coal dust datasets and the labor-intensive process of real-world data collection, a point cloud data augmentation method is proposed based on the scattering and absorption effects of coal dust on LiDAR electromagnetic waves. By incorporating the optical characteristics of coal dust particles, we develop a propagation simulation model to characterize LiDAR signal attenuation and scattering phenomena within coal dust-laden atmospheres. Real point cloud data acquired under clear weather conditions are subsequently transformed via the simulation model, adjusting 3D coordinates and reflection intensities to synthesize coal dust-adaptive point clouds. Five state-of-the-art models (PV-RCNN++, PV-RCNN, PointRCNN, PointPillars, Voxel-RCNN_Car) are trained on the augmented dataset, demonstrating consistent accuracy improvements in coal dust scenarios. The most architecturally complex model, PV-RCNN, improved performance by 1.88, 1.74, and 0.84 percentage points in the car, pedestrian, and cyclist categories, respectively . Results indicate that using the augmented point cloud data to train object detection models significantly improves detection accuracy in coal dust environments in comparison with models trained in clear conditions.The proposed methodology enables robust perception in open-pit mining operations, thereby advancing reliable data support for autonomous mining vehicle deployment.

Key words: autonomous mining truck, LiDAR, three-dimensional point cloud, data augmentation, physics simulation, object detection