Multi-Drone 3D Building Reconstruction Method
2. State of the Art
- Workload distribution among drones;
- Localization and control of each drone;
- 3D scanning by using only simple drone sensors;
- Usage of any additional hardware apart from drones and a control server;
- Automation of the whole process.
- Mention scanning large objects;
- Mention the usage of several drones;
- Describe industrial or research reconstruction solutions.
- What multiagent approach is used? This question includes subquestions about a workload distribution, a trajectory planning, a collision avoidance, an accuracy, and the speed of the whole process;
- What localization approach is used?;
- What 3d reconstruction approach is used?;
- What is the level of solution automation? This question includes subquestions about prerequisites (including preconfiguration, calibration, building, and teaching models), the need of human attention during the process and abilities for customizing the solution for different objects and environments;
- What limitations does a solution have? This question addresses the usage of any additional hardware or external spatial markup.
2.1. What Multiagent Approach Is Used?
2.2. What Localization Approach Is Used?
2.3. What 3D Reconstruction Approach Is Used?
2.4. What Is the Level of Solution Automation
2.5. What Are the Limitations of a Solution?
- The solution should use homogeneous architecture with centralized control in order to support low-cost drones;
- The solution should provide a two-step 3D reconstruction process (online and offline);
- Online 3D reconstruction process should be a reasonable trade-off between 3D map accuracy and computing complexity;
- Offline 3D reconstruction process should use photogrammetry because it allows scaling the number of drones and input data volume;
- Due to the usage of low-cost drones it is important to use RGB images as a main data source because it is available on most platforms instead of complex sensors (LiDAR, RGBD, GPS).
3. The Essence of the Proposed Method
3.1. Trajectory Building
3.2. The Localization Approach
3.3. 3D Reconstruction Approach for Offline Map
3.4. Method Scalability and Adaptation
- Divide the subarea approximately into three parts—front side, roof, and back side of the building.;
- Divide the front side and back side parts of the subareas evenly and horizontally (division lines are parallel X axis) in the ZX plane;
- Divide the roof part in the XY plane evenly and vertically (division lines are parallel Y axis) for subsubareas.
- Each drone is assigned to a particular subsubarea;
- The system calculates deploying trajectories for each drone from the base to the approximate starting point of a subsubarea (bottom-left corner for frontside and backside subsubareas, front-left corner for roof subsubareas);
- Each drone on frontside and backside subsubareas starts scanning using a snake pattern moving from left to right and from down to up;
- Each drone on roof subsubareas starts scanning using a snake pattern moving from left to right and from front to back;
- After completing the subsubarea drone returns to the base.
4.1. Trajectory Benefits
- S—the distance that the drone might process;
- v—the average speed of the drone;
- —the estimated time resource of the battery.
- N—amount of spiral turns in a vertical plane;
- M—amount of spiral turns in a horizontal plane;
- —widths of the drone’s subarea;
- —height of a building;
- y—width of a building.
4.2. Experiments in a Simulation
- Knowing the rough boundaries of a building 60 × 40 × 30, the area was divided into 3 parts—each for one drone;
- The trajectory for each drone was constructed independently. The beginning point for each drone is set manually according to the division on sub-areas;
- Each drone follows the trajectory, keeping a constant distance to the building. If the distance is changed (i.e., because of unevenness of a building wall), the trajectory of the drone is updated to keep the constant distance;
- After the drone reaches the end of its trajectory, it returns back to the point of the beginning;
- Captured images are provided to Meshroom  to construct a 3D model of a building.
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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|Drone 1||Drone 2||Drone 3|
|MAE of trajectory, m|
|Max error of trajectory, m||0.70||0.74|
|X, m||Y, m||Z, m||Number||Trajectory||Distance to||Overlap||Expected Quality|
|of Drones||Resource, m||the Building, m||of Images||of Photogrammetry|
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Filatov, A.; Zaslavskiy, M.; Krinkin, K. Multi-Drone 3D Building Reconstruction Method. Mathematics 2021, 9, 3033. https://doi.org/10.3390/math9233033
Filatov A, Zaslavskiy M, Krinkin K. Multi-Drone 3D Building Reconstruction Method. Mathematics. 2021; 9(23):3033. https://doi.org/10.3390/math9233033Chicago/Turabian Style
Filatov, Anton, Mark Zaslavskiy, and Kirill Krinkin. 2021. "Multi-Drone 3D Building Reconstruction Method" Mathematics 9, no. 23: 3033. https://doi.org/10.3390/math9233033