Method for Improving Digital Elevation Model Precision of Consumer Level Unmanned Aerial Vehicles

Currently,the aerial survey system of low-altitude unmanned aerial vehicles(UAVs)has been widely used in acquiring digital map 4D products,mapping,digital linear maps,and other aspects.However,there are problems,such as low precision and weak practicability in constructing digital elevation model(DEM)products through the data collected using consumption level UAVs.Therefore,improving the accuracy of DEM products obtained by consumption level UAVs is a crucial and complex issue in the research of UAV aerial survey systems.In precision elevation measurement,the geodetic height of a certain number of ground points with reasonable distribution in the region is often obtained first.Then,the normal height of the ground points is obtained by leveling,and the elevation residual value surface of the region is fitted.Finally,the normal height of the points to be solved in the region is obtained by fitting the elevation residual surface.Therefore,the elevation residual fitting method was used to improve the accuracy of consumer UAV DEM products in this study.First,a high-quality ground point cloud was obtained by constructing the gradient filtering-cloth simulation filtering(GF-CSF)model.Second,an abnormal elevation fitting residual DEM model was constructed.Lastly,the final DEM was obtained using the DEM difference method.The experimental results show that among the 20 random sampling inspection points,the average elevation residual was 2.3 mm,and the root mean square error(RMSE)was 16.7 mm after the DEM accuracy was improved by the method.The average elevation residual without improving the DEM accuracy was 28.6 mm,and RMSE was 33.7 mm.

Applications of intelligent computing in vehicular networks

Purpose–This paper aims to introduce vehicular network platform,routing and broadcasting methods and vehicular positioning enhancement technology,which are three aspects of the applications of intelligent computing in vehicular networks.From this paper,the role of intelligent algorithm in thefield of transportation and the vehicular networks can be understood.Design/methodology/approach–In this paper,the authors introduce three different methods in three layers of vehicle networking,which are data cleaning based on machine learning,routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles.Findings–In Section 2,a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database.In Section 3,the authors canfind when traffic conditions varied from freeflow to congestion,the number of message copies increased dramatically and the reachability also improved.The error of vehicle positioning is reduced by 35.39%based on the CV-IMM-EKF in Section 4.Finally,it can be concluded that the intelligent computing in the vehicle network system is effective,and it will improve the development of the car networking system.Originality/value–This paper reviews the research of intelligent algorithms in three related areas of vehicle networking.In thefield of vehicle networking,these research results are conducive to promoting data processing and algorithm optimization,and it may lay the foundation for the new methods.