Next Article in Journal
Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network
Next Article in Special Issue
Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic
Previous Article in Journal
Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series
Previous Article in Special Issue
Deep Gene Networks and Response to Stress

Multi-Drone 3D Building Reconstruction Method

Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University “LETI”, 197022 Saint Petersburg, Russia
Author to whom correspondence should be addressed.
Academic Editors: Ovanes Petrosian, António Mendes Lopes and Witold Pedrycz
Mathematics 2021, 9(23), 3033;
Received: 12 October 2021 / Revised: 19 November 2021 / Accepted: 23 November 2021 / Published: 26 November 2021
(This article belongs to the Special Issue Application of Mathematical Methods in Artificial Intelligence)
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. View Full-Text
Keywords: drone; multi-drone; structure from motion; photogrammetry drone; multi-drone; structure from motion; photogrammetry
Show Figures

Figure 1

MDPI and ACS Style

Filatov, A.; Zaslavskiy, M.; Krinkin, K. Multi-Drone 3D Building Reconstruction Method. Mathematics 2021, 9, 3033.

AMA Style

Filatov A, Zaslavskiy M, Krinkin K. Multi-Drone 3D Building Reconstruction Method. Mathematics. 2021; 9(23):3033.

Chicago/Turabian Style

Filatov, Anton, Mark Zaslavskiy, and Kirill Krinkin. 2021. "Multi-Drone 3D Building Reconstruction Method" Mathematics 9, no. 23: 3033.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop