Multi-Drone Camera Pose Alignment for 3D Scene Reconstruction
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Solution Overview
Problem
Existing systems struggle to integrate and control multiple drone cameras for efficient 3D scene reconstruction, requiring manual operation and lacking automatic feedback mechanisms for precise drone positioning and camera pose estimation.
Innovation Solution
A system that uses drone agents and a global optimizer to align camera poses and generate 3D point clouds by combining SLAM and MultiView Triangulation, enabling automatic control of drone trajectories and camera formations based on visual content, without relying on depth sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple drones are used for simultaneous capturing at different viewing spots, then the quality of 3D scene reconstruction is improved, but the complexity of integrating video streams and controlling drone formations increases
Solution Approach 1:
The system employs feedback mechanisms where each drone's estimated pose is used as a reference frame to express all other poses and feature locations. The global pose estimation continuously updates and refines the coordinate transformations between multiple drones, creating a closed-loop control system that adapts to aerodynamic noise and maintains formation accuracy.
Solution Approach 2:
The patent introduces an intermediary coordinate transformation system that mediates between multiple drone coordinate frames. By establishing a global reference frame and computing transformations between local and global coordinates, the system enables seamless integration of visual content from multiple drones without direct complex interactions between each drone pair.
2Device complexity
If conventional SLAM approaches are used for single-drone positioning, then the implementation is simple, but the system cannot estimate poses for multiple drones or multiple cameras simultaneously
Solution Approach 1:
The system creates a universal pose estimation framework that handles both single-drone and multi-drone scenarios through the same mathematical foundation. The coordinate transformation and global optimization methods work uniformly whether one drone or multiple drones are operating, allowing the system to scale from simple to complex configurations without requiring different algorithms.
Solution Approach 2:
The patent merges SLAM techniques with multi-view geometry methods to create a unified system. By combining local SLAM pose estimation with global multi-drone coordinate transformation and optimization, the system integrates multiple functionality into a single framework that simultaneously achieves localization, mapping, and formation control.
3Measurement precision
If human operators manually control each drone's flight parameters and camera pose separately, then the control precision can be maintained, but the system size, weight, cost, and reliability deteriorate
Solution Approach 1:
The system enables self-service operation where drones automatically adjust their flight parameters and camera poses based on visual feedback and computed transformations. The autonomous coordination eliminates the need for manual intervention in formation maintenance and pose synchronization, reducing operator workload while maintaining precision through algorithmic control.
Solution Approach 2:
The patent replaces manual mechanical control systems with automated computational control. Instead of operators physically manipulating multiple controllers, the system uses computer vision, coordinate transformation, and automated feedback loops to control drone positions and camera poses, reducing hardware complexity and improving reliability.
Data Source
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AI summary
A system of imaging a scene includes a plurality of drones, each drone moving along a corresponding flight path over the scene and having a drone camera capturing, at a corresponding first pose and first time, a corresponding first image of the scene; a fly controller that controls the flight path of each drone, in part by using estimates of the first pose of each drone camera provided by a camera controller, to create and maintain a desired pattern of drones with desired camera poses; and the camera controller, which receives, from the drones, a corresponding plurality of captured images, processing the received images to generate a 3D representation of the scene as a system output, and to provide the estimates of the first pose of each drone camera to the fly controller. The system is fully operational with as few as one human operator.