-Last decade saw a considerable growth in number of methods to create 3D models for buildings. Some companies have developed their own online maps systems. One of the most well-known of them is Google Maps. The maps are infrequently updated because the process is costly and labor-consuming, and, as a rule, the update is not necessary. The process of 3D model creation for a separate building using such equipment as LIDAR and UltraCamD is very expensive; and land-based aerophotogrammetry has its limitations based on the height of the building and its location that should be suitable to place the camera to get an accurate photos.The article presents results of using 3D reconstruction technique based on 2D pictures of a building taken by a quadcopter . The latter is an unmanned aerial vehicle (UAV) being used to survey buildings of various heights without interfering with public transport. Moreover, the quadcopter can reach any position required to take the necessary angle for a favourable photos.The building under consideration has been surveyed using Autodesk 123D package to reconstruct 3D geometry of it. The results prove that the quality of regenerated pictures taken by quadcopter is comparable to those received using UltraCamD and Pictometry equipment, façade pictures being even of better quality. The modeling results show good image quality and sufficient area of overlap which makes the model more real.
This article studies the development of a cyberphysical system for diagnosing and forecasting the technical condition of electric drives in robots used for construction. The structure of the cyber-physical system is described and the defect statistics for asynchronous drives submitted. Additionally, a critical analysis of existing diagnostic methods, the selection of the optimal set of diagnostic parameters and existing methods for measuring and analyzing the parameters used for the drives adopted in construction robots for is described. Herein, a method capable of distinguishing a faulty state from a load change based on wavelet transformation and neural networks (diagnosing the technical condition of robot drives) is proposed. Finally, we provide the simulation and experimental results of the proposed method.
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