UAV GPS Attitude Estimation for Real-Time Computer Vision
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Solution Overview
Problem
Unmanned autonomous vehicles (UAVs) face challenges in implementing real-time computer vision due to resource constraints, such as weight and energy limitations, making it difficult to perform precise attitude estimation and camera pose rotation for effective engagement with their environment.
Innovation Solution
The method involves using multiple GPS receivers mounted on the UAV to estimate the UAV's attitude by determining carrier phase and pseudo-range measurements, which are then used to calculate a 3D camera pose rotation matrix, enabling real-time computer vision image search computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision heuristics are used for precise attitude estimation and camera pose rotation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces GPS attitude estimation as an intermediary mechanism that provides camera pose rotation information to computer vision processing. By using GPS receivers to determine UAV attitude and generating rotation matrices, the system creates a mediator that bridges the gap between raw sensor data and computer vision heuristics, reducing the computational burden while maintaining precision
Solution Approach 2:
The system performs preliminary attitude estimation using GPS receivers before computer vision processing occurs. By pre-calculating camera pose rotation matrices from GPS data, the system prepares transformation information in advance, allowing computer vision heuristics to operate more efficiently with reduced computational complexity
2Productivity
If powerful back-end cloud compute systems are used for computer vision, then productivity is improved, but weight increases
Solution Approach 1:
The patent implements self-service by enabling the UAV to perform computer vision processing locally using its own onboard resources. The system uses GPS attitude estimation and camera pose rotation to accelerate computer vision computations directly on the UAV platform, eliminating the need for external cloud compute systems and maintaining real-time processing capability while keeping the system lightweight
3Measurement precision
If high performance server with GPUs is used for computer vision, then measurement precision is improved, but weight increases
Solution Approach 1:
The patent substitutes the mechanical/GPU-based computer vision processing system with a GPS-based attitude estimation approach. By replacing the need for heavy GPU computations with GPS receiver-based attitude determination and rotation matrix calculations, the system achieves comparable measurement precision with significantly reduced weight and computational hardware requirements
Data Source
AI summary
A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving GPS signals from each GPS satellite in view of the UAV, the GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.