Multi-Drone Camera Pose Control Without Image Analysis
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Solution Overview
Problem
Current drone cinematography systems face challenges in accurately capturing targets due to incorrect camera poses and inadequate control of drone movements and camera parameters, especially in multi-drone setups with moving targets, relying heavily on scripted movements and computationally intensive visual data analysis.
Innovation Solution
A method involving real-time determination of drone and camera locations, calculation of distances between the target and drones, and adaptive control of camera poses and image capture parameters without image analysis, allowing for precise and automated image capture of moving targets using techniques like RTK-GNSS and IMU, enabling efficient real-time image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scripted movements and visual data analysis are used for multi-drone camera control, then coordination accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts and removes the computationally intensive visual data analysis step from the control system. Instead of analyzing captured images to determine target position and camera pose, the system uses independent localization techniques (such as GPS, visual markers, or other tracking methods) to directly obtain target location data. This separation eliminates the need for real-time image processing while maintaining accurate target localization.
Solution Approach 2:
The system performs preliminary localization of both the target and drones using techniques independent of image capture. By determining positions and poses before image analysis (or without requiring it), the system prepares all necessary spatial data in advance, enabling direct calculation of camera poses and image capture parameters without iterative visual feedback loops.
2Manufacturing precision
If real-time adaptive control of camera pose and image capture parameters is implemented, then image quality is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system implements feedback by continuously monitoring the calculated distance between the drone and target, and using this information to dynamically adjust camera pose and image capture parameters. The controller receives real-time position data from localization techniques, calculates the required camera adjustments, and applies these corrections in a closed-loop manner to maintain optimal image quality throughout the capture sequence.
Solution Approach 2:
The patent dynamically changes camera parameters (such as focal length, aperture, shutter speed, and zoom) based on the calculated distance to the target. By adjusting these parameters in real-time according to the drone-target separation distance, the system maintains consistent image quality and proper subject framing without requiring complex control algorithms, as the parameter adjustments are directly derived from the distance calculation.
3Measurement precision
If computationally intensive visual data analysis is used for target tracking, then tracking accuracy is improved, but processing speed and response time decrease
Solution Approach 1:
The patent removes the visual data analysis step from the target tracking process. Instead of capturing and analyzing images to determine target position, the system uses independent localization techniques (such as GPS tracking, visual markers, or radio frequency identification) to directly obtain real-time target location data. This extraction of the image analysis component eliminates computational delays while maintaining continuous tracking capability.
4Productivity
If automated camera control without image analysis is implemented, then processing speed is improved, but adaptability to unexpected occurrences decreases
Solution Approach 1:
The system performs self-service by automatically calculating camera poses and adjusting image capture parameters based on real-time distance measurements between the drone and target. The controller autonomously determines the necessary camera adjustments and executes them without requiring human intervention, enabling rapid automated operation while maintaining adaptability through continuous real-time parameter adjustment based on changing flight conditions and target positions.
Data Source
AI summary
A method of camera control for a camera capturing images of a target includes a sequence of at least four steps. The first step determines in real time a location of a drone and a pose of a camera on the drone. The second step, which may occur before or after the first step, determines in real time a location of a reference object, the location of the reference object having a fixed relationship to a location of the target. The third step uses the determined locations to calculate a distance, characterized by magnitude and direction, between the target and the drone. The fourth step uses the calculated distance to control the pose of the camera such that an image captured by the camera includes the target. Controlling the pose of the camera does not require any analysis of the captured image.