Point cloud data augmentation method based on scattering and absorption effects of coal dust on LiDAR electromagnetic waves

doi:10.11772/j.issn.1001-9081.2025010085

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (1): 331-340.DOI: 10.11772/j.issn.1001-9081.2025010085

• Frontier and comprehensive applications • Previous Articles

Point cloud data augmentation method based on scattering and absorption effects of coal dust on LiDAR electromagnetic waves

Shiwei LI¹, Yufeng ZHOU¹, Pengfei SUN¹, Weisong LIU², Zhuxuan MENG², Haojie LIAN¹()

^1.Key Laboratory of In-situ Property-improving Mining of Ministry of Education （Taiyuan University of Technology），Taiyuan Shanxi 030024，China
^2.Academy of Military Science，Beijing 100091，China

Received:2025-01-22 Revised:2025-03-21 Accepted:2025-03-27 Online:2026-01-10 Published:2026-01-10
Contact: Haojie LIAN
About author:LI Shiwei， born in 1999， M. S. candidate. His research interests include deep learning.
ZHOU Yufeng， born in 2003， M. S. candidate. His research interests include deep learning.
SUN Pengfei， born in 1997， M. S. candidate. His research interests include deep learning.
LIU Weisong， born in 1993， Ph. D.， assistant research fellow. His research interests include information assessment， numerical computation.
MENG Zhuxuan， born in 1990， Ph. D.， assistant research fellow. Her research interests include information assessment， numerical computation.
Supported by:
National Natural Science Foundation of China(52274222)

基于煤尘对激光雷达电磁波散射和吸收效应的点云数据增强方法

李世伟¹, 周昱峰¹, 孙鹏飞¹, 刘伟松², 孟竹喧², 廉浩杰¹()

^1.原位改性采矿教育部重点实验室（太原理工大学），太原 030024
^2.中国人民解放军军事科学研究院，北京 100091

通讯作者: 廉浩杰
作者简介:李世伟（1999—），男，山西晋城人，硕士研究生，主要研究方向：深度学习
周昱峰（2003—），男，山西运城人，硕士研究生，主要研究方向：深度学习
孙鹏飞（1997—），男，山西运城人，硕士研究生，主要研究方向：深度学习
刘伟松（1993—），男，山东济宁人，助理研究员，博士，主要研究方向：信息评估、数值计算
孟竹喧（1990—），女，吉林白山人，助理研究员，博士，主要研究方向：信息评估、数值计算
基金资助:
国家自然科学基金资助项目(52274222)

Abstract

Abstract:

Current 3D object detection models are predominantly based on data-driven deep learning techniques， so that dataset quality plays a pivotal role in model performance. Aiming at the scarcity of coal dust environment data and the time-consuming and labor-intensive construction process of real-world datasets， a point cloud data augmentation method was proposed on the basis of scattering and absorption effects of coal dust on Light Detection and Ranging （LiDAR） electromagnetic waves. In the method， by considering the optical characteristics of coal dust particles， a propagation simulation model of LiDAR electromagnetic waves was built to characterize LiDAR signal attenuation and scattering phenomena in coal dust environment. Secondly， based on real point cloud data acquired under clear weather conditions， 3D coordinates and reflection intensities were adjusted via the simulation model， so as to generate simulated point cloud data that conforms to the perception characteristics of coal dust environment. Finally， five mainstream 3D models （PV-RCNN++， PV-RCNN， PointRCNN， PointPillars， Voxel-RCNN_Car） were trained and tested on the augmented dataset. The results demonstrate that the proposed method improves the detection precision of these five detection models in coal dust environment. For the most complex model， PV-RCNN， improved performance is 1.88， 1.74， and 0.84 percentage points in the car， pedestrian， and cyclist categories， respectively. It can be seen that in comparison with models trained in clear weather conditions， using the augmented point cloud data to train object detection models improves detection precision in coal dust environment significantly， so as to enable more reliable perception in complex environments in open-pit mines， thereby providing data support for stable operation of autonomous mine carts.

Key words: autonomous mine cart, Light Detection and Ranging (LiDAR), 3D point cloud, data augmentation, physics simulation, object detection

摘要：

当前的三维目标检测模型大都基于数据驱动的深度学习技术，因此数据集的质量对模型的性能至关重要。针对煤尘环境数据集缺失和建立真实煤尘环境数据集费时费力的问题，提出一种基于煤尘对激光雷达（LiDAR）电磁波散射和吸收效应的点云数据增强方法。该方法针对煤尘粒子的光学特性，构建LiDAR电磁波在煤尘中的传播仿真模型，从而模拟LiDAR信号在煤尘环境中的衰减与散射；然后，在晴朗环境下采集的真实点云数据基础上，基于仿真模型对点云的三维坐标和反射强度进行修正，从而生成符合煤尘环境感知特性的仿真点云数据；最后，在增强后的仿真数据集上训练并测试5种主流三维目标检测模型（PV-RCNN++、PV-RCNN、PointRCNN、PointPillars和Voxel_RCNN_Car）。结果表明，所提方法让这5种检测模型在煤尘环境下的检测精度均有所提升，其中模型复杂度最高的PV-RCNN模型在汽车、行人和骑行者类别上的中等难度表现分别提高了1.88、1.74和0.84个百分点。可见，在煤尘环境中，相较于在晴朗条件下训练的模型，使用增强后的点云数据训练的目标检测模型的检测精度有显著提升，能更可靠地感知露天矿复杂环境，为无人驾驶矿车的稳定运行提供了数据支撑。

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

CLC Number:

TP391.9

Shiwei LI, Yufeng ZHOU, Pengfei SUN, Weisong LIU, Zhuxuan MENG, Haojie LIAN. Point cloud data augmentation method based on scattering and absorption effects of coal dust on LiDAR electromagnetic waves[J]. Journal of Computer Applications, 2026, 46(1): 331-340.

李世伟, 周昱峰, 孙鹏飞, 刘伟松, 孟竹喧, 廉浩杰. 基于煤尘对激光雷达电磁波散射和吸收效应的点云数据增强方法[J]. 《计算机应用》唯一官方网站, 2026, 46(1): 331-340.

Figures/Tables 16

Fig. 1 Scattering effect of scattered particles on pulses

Fig. 2 Example of snow simulation algorithm［14］

Fig. 3 Basic environments of open-pit mines

Fig. 4 Schematic diagram of coal dust optical characteristics analysis experiment［21］

Tab. 1 Calculation for extinction coefficients［21］ and weights

粒径大小/μm	$a 0$	$a 1 / 10 - 4$	权重
（0，20］	0.006 1	9.5	0.689
（20，45］	0.044 5	10.7	0.279
（45，60］	0.000 0	19.2	0.022
（60，90］	-0.008 8	25.2	0.009
（90，125］	-0.000 2	34.3	0.001
（125，150］	0.024 6	36.8	0.000
（150，180］	0.030 3	44.4	0.000
（212，250］	0.023 7	216.6	0.000

Tab. 1 Calculation for extinction coefficients［21］ and weights

粒径大小/μm	$a 0$	$a 1 / 10 - 4$	权重
（0，20］	0.006 1	9.5	0.689
（20，45］	0.044 5	10.7	0.279
（45，60］	0.000 0	19.2	0.022
（60，90］	-0.008 8	25.2	0.009
（90，125］	-0.000 2	34.3	0.001
（125，150］	0.024 6	36.8	0.000
（150，180］	0.030 3	44.4	0.000
（212，250］	0.023 7	216.6	0.000

Fig. 5 Schematic diagram of LiDAR sensor with dual-beam configuration［23］

Fig. 6 Comparison of received signal power of single LiDAR pulse［13］

Fig. 7 Flow of point cloud simulation algorithm in coal dust environment

Fig. 8 Camera perspective

Fig. 9 Simulation results of AutoMine dataset （sample 1）

Fig. 10 Simulation results of AutoMine dataset （sample 2）

Fig. 11 Simulation results of KITTI dataset （sample 1）

Fig. 12 Simulation results of KITTI dataset （sample 2）

Fig. 13 Simulation results of KITTI dataset （sample 3）

Tab. 2 Detection precisions of 3D object detection models in coal dust environment

模型	数据	汽车 AP@0.7IoU			行人AP@0.25IoU			骑行者AP@0.25IoU
模型	数据	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
PV-RCNN++	原始数据	95.89	85.88	86.23	63.04	45.90	42.60	72.51	48.16	45.02
PV-RCNN++	仿真数据	96.01	86.35	86.41	64.31	51.42	47.56	74.96	49.68	46.54
PV-RCNN	原始数据	91.88	80.10	77.41	50.57	43.89	39.97	70.35	51.67	48.22
PV-RCNN	仿真数据	92.90	81.98	78.26	54.06	45.63	40.88	70.95	52.51	49.97
PointRCNN	原始数据	88.52	74.60	70.56	49.62	43.24	38.18	69.28	49.47	46.26
PointRCNN	仿真数据	89.68	75.27	71.72	50.89	44.39	39.59	68.36	50.89	47.33
PointPillars	原始数据	87.25	72.86	68.32	35.00	29.81	27.44	63.28	43.24	40.22
PointPillars	仿真数据	87.98	73.90	69.16	35.88	31.20	29.49	59.70	43.83	39.75
Voxel_RCNN_Car	原始数据	98.56	92.85	91.12
Voxel_RCNN_Car	仿真数据	98.38	93.57	91.68

Tab. 3 Detection precisions from bird's-eye view in coal dust environment

模型	数据	汽车AP@0.7IoU			行人AP@0.25IoU			骑行者AP@0.25IoU
模型	数据	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
PV-RCNN++	原始数据	96.03	86.29	86.80	63.06	45.95	42.66	72.51	48.16	45.02
PV-RCNN++	仿真数据	96.04	86.99	86.97	64.31	51.60	47.67	74.96	49.68	46.54
PV-RCNN	原始数据	95.12	87.24	84.93	58.04	51.14	47.22	70.56	52.89	50.10
PV-RCNN	仿真数据	96.41	88.38	86.14	58.60	51.71	46.04	71.50	53.97	51.77
PointRCNN	原始数据	92.48	83.19	79.23	52.66	47.52	41.27	71.38	52.09	48.67
PointRCNN	仿真数据	93.58	84.21	79.88	54.50	48.63	42.63	74.00	55.90	52.89
PointPillars	原始数据	92.09	85.05	82.00	44.63	38.75	35.73	66.85	48.11	44.94
PointPillars	仿真数据	93.55	85.94	83.16	45.77	39.76	36.13	61.28	48.90	44.23
Voxel_RCNN_Car	原始数据	98.60	93.09	91.28
Voxel_RCNN_Car	仿真数据	98.41	93.76	91.81

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Point cloud data augmentation method based on scattering and absorption effects of coal dust on LiDAR electromagnetic waves

基于煤尘对激光雷达电磁波散射和吸收效应的点云数据增强方法

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