Research on Filling Method for Missing Data of LiDAR Sensor in New Energy Vehicle
In order to enhance the comprehensiveness of vehicle LiDAR sensor data collection,a method for filling missing data in new energy vehicle LiDAR sensors is proposed.The method utilizes data fusion-based point cloud acquisition techniques and median filtering for preprocessing the point cloud data.An improved noise density clustering algorithm is employed to construct point cloud supervoxels,build a graph model,and perform global clustering using graph cuts.By extracting typical features of objects and densely matching the missing regions using panoramic images,the missing areas in the point cloud data are filled.Experimental results demonstrate that the proposed method effectively fills missing data with good results.The filled point cloud data closely approximates the depth distribution of the original point cloud in the missing regions.
new energy vehicleLiDARsensorfilling in missing datapoint cloud collectionpoint cloud denoising
万方数据
维普
