UAV Velocity Detection via Visual-Inertial Offset Compensation
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Solution Overview
Problem
Current Unmanned Aerial Vehicle (UAV) velocity detection and positioning control rely heavily on GPS, which is inadequate in environments without GPS signals, and existing methods lack precision in various scenes.
Innovation Solution
A flight device equipped with a processor, camera module, distance sensor, and acceleration sensor that calculates velocity by determining image offsets from adjacent frames, compensating for camera movements, and converting these offsets into world coordinates to derive velocity, enabling precise positioning control without GPS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS-based velocity detection and positioning control is used, then the system is simple and easy to operate, but it becomes unreliable in environments without GPS signals
Solution Approach 1:
The patent replaces the GPS-based positioning system with a visual-inertial navigation system that uses camera modules to capture images and acceleration sensors to detect motion. This substitution enables the UAV to determine its position and velocity through image processing and sensor fusion algorithms, making the system reliable in environments where GPS signals are unavailable.
Solution Approach 2:
The patent introduces image processing algorithms and sensor fusion mechanisms as intermediaries between the physical motion of the UAV and the calculation of its position and velocity. By using feature point extraction, optical flow analysis, and compensation for camera movements, the system mediates the relationship between visual data and navigation parameters, achieving accurate positioning without GPS.
2Measurement precision
If general scene-based algorithms are used for velocity detection, then the system is easy to implement, but it lacks precision in various specific scenes
Solution Approach 1:
The patent implements scene-specific algorithms tailored to different environments such as ground scenes, water scenes, and building scenes. Each scene type has optimized feature extraction and matching strategies that consider the unique characteristics of that environment, thereby improving velocity detection precision while managing algorithm complexity through selective application.
Solution Approach 2:
The patent employs dynamic scene recognition that automatically identifies the current environment type and switches between different processing algorithms accordingly. This dynamic adaptation allows the system to maintain high precision across various scenes without requiring manual configuration, balancing accuracy with operational simplicity.
3Measurement precision
If image offsets are calculated without compensation, then the calculation is simple, but the velocity derivation is inaccurate due to camera movements
Solution Approach 1:
The patent applies preliminary compensation for camera movements by using acceleration sensor data to predict and correct for vibrations and姿态 changes before calculating velocity from image offsets. This pre-compensation approach counteracts the harmful effects of camera instability, ensuring more accurate velocity measurements while maintaining a relatively simple processing pipeline.
Data Source
AI summary
A flight device and a flight control method are disclosed. The method includes: acquiring an image and a height of the flight device; determining a scene in which the flight device is currently located; calculating an image X-offset and an image Y-offset of a second image frame of two adjacent image frames relative to a first image frame of the two adjacent image frames; compensating for the image X-offset and the image Y-offset according to the acceleration and the angular velocity of the flight device to obtain image correction offsets comprising a corrected image X-offset and a corrected image Y-offset; calculating an X-offset and a Y-offset in world coordinates corresponding to the image correction offsets; and deriving a velocity of the flight device.