In the recent decade, the rapid development of drone technologies has made many spatial problems easier to solve, including the problem of 3D reconstruction of large objects. A review of existing solutions has shown that most of the works lack the autonomy of drones because of nonscalable mapping techniques. This paper presents a method for centralized multi-drone 3D reconstruction, which allows performing a data capturing process autonomously and requires drones equipped only with an RGB camera. The essence of the method is a multiagent approach—the control center performs the workload distribution evenly and independently for all drones, allowing simultaneous flights without a high risk of collision. The center continuously receives RGB data from drones and performs each drone localization (using visual odometry estimations) and rough online mapping of the environment (using image descriptors for estimating the distance to the building). The method relies on a set of several user-defined parameters, which allows the tuning of the method for different task-specific requirements such as the number of drones, 3D model detalization, data capturing time, and energy consumption. By numerical experiments, it is shown that method parameters can be estimated by performing a set of computations requiring characteristics of drones and the building that are simple to obtain. Method performance was evaluated by an experiment with virtual building and emulated drone sensors. Experimental evaluation showed that the precision of the chosen algorithms for online localization and mapping is enough to perform simultaneous flights and the amount of captured RGB data is enough for further reconstruction.

Introduction. The use of available satellite images and aerial photography by unmanned aerial vehicles (UAVs) in the tasks of environmental monitoring is challenged by the imperfection of existing tools. Geographic information systems are characterized by insufficient flexibility to automatically work with heterogeneous sources. The latest models based on artificial intelligence in ecology require preliminary data preparation. The article presents the results of designing a software system for environmental monitoring based on machine vision sensor data, which provides data unification while being flexible both in terms of data sources and methods of their analysis.Aim. Creation of a generalized software system for coordinated spatial marking of heterogeneous machine vision data for environmental monitoring tasks.Materials and methods. Software engineering methods, database theory methods, spatial markup methods, image processing methods.Results. A generalized method for unifying data was developed. The method is based on the analysis of existing open data from remote sensing of the Earth, as well as UAV aerial photography and approaches to environmental monitoring. To implement the method, a flexible architecture of the software system was designed, and a data model for a document-oriented DBMS was developed, which allows storing data and scaling the data analysis procedure.Conclusion. The existing sources of data and tools for environmental monitoring were analyzed. A generalized method for unifying machine vision data, an architecture, and a data model was created. The method, architecture, and model were successfully implemented as a software system with a web interface