Modeling and Verification of Dynamic Imaging of UAV-borne Line-array LiDAR

doi:10.12382/bgxb.2024.0836

Abstract

Abstract:

A dynamic imaging model for UAV(Unmanned Aerial Vehicle)-borne line-array LiDAR is proposed to address the scarcity of point cloud datasets in UAV imaging scene.A pillar-voxel-based point cloud target detection algorithm is proposed to verify the authenticity of the simulation data.The typical target models and the typical scenarios with rugged terrain,camouflage,and vegetation cover are established based on a virtual simulation platform.A laser point cloud dataset is generated by using the point cloud simulation model,UAV motion model,stitching imaging and distortion correction methods.The dataset is annotated by using a completeness judgment method based on overlapping areas.A pilar-voxel feature extraction module is used to process the target’s top features.The point cloud target detection algorithm is trained using the annotated simulation dataset,and evaluated on a real dataset obtained from equivalent experiments.The proposed algorithm achieves an accuracy rate of 93.2% on the real dataset.The evaluated result indicates that the simulated data effectively reflect the true characteristics of the targets,and the dynamic imaging model has high credibility.

Key words: line-array LiDAR, pushbroom imaging, laser point cloud, unmanned aerial vehicle, target recognition

CLC Number:

TJ43⁺9.2

WANG Weihan, GAO Mingze, SHI Xiaolong, HU Shiyuan, WU Yanjiang, CHEN Huimin. Modeling and Verification of Dynamic Imaging of UAV-borne Line-array LiDAR[J]. Acta Armamentarii, 2025, 46(6): 240836-.

Figures/Tables 18

Fig.1 Schematic diagram of pushbroom imaging

Fig.2 Scene of UAV and target rendezvous

Fig.3 Typical target model

Fig.4 Construction of rugged terrain scenes

Fig.5 Construction of camouflage and vegetation cover scene

Fig.6 Emission direction vector cluster

Fig.7 Point clouds with different attack angles in the coordinate system of LiDAR

Fig.8 Point cloud after distortion correction in the target coordinate system

Fig.9 Pillar-voxel-based feature extraction method

Fig.10 Schematic diagram of backbone network

参数	数值	参数	数值
脱靶量/m	5	激光重频/Hz	360
脱靶方位/(°)	0~180	起始位置/m	10
交会角度/(°)	0~360	水平视场角/(°)	60
飞行速度/(m·s^-1)	10	激光线数	300
攻击角/(°)	0~60	探测距离/m	20

Fig.11 Simulated results under typical targets and parameters

Fig.12 Simulation point cloud under typical environmental interference

Fig.13 Iterative curves of recognition accuracy for tank and car

Fig.14 Recognition effects under various typical scenarios and parameters

Fig.15 Equivalent experimental imaging scene of line-array LiDAR

Fig.16 Measured and simulated data

Fig.17 Recognition effect of measured point cloud

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