机载线阵激光雷达动态成像建模与验证

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.