Adaptive Alignment System for IMU-Video Sensor Misalignment
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Solution Overview
Problem
Existing sensor systems, such as those using Infrared Search and Track (IRST) sensors, face challenges in accurately aligning Inertial Measurement Unit (IMU) data with video or sensor data, leading to potential misalignment and reduced accuracy in threat detection and geospatial coordination.
Innovation Solution
An adaptive alignment system that receives video data and IMU data, adjusts video frames based on IMU-induced motion, and determines frame adjustment values to improve alignment between IMU and sensor data, ensuring accurate geospatial coordination and threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment calibration is used between IMU and sensor data, then the alignment accuracy is maintained under static conditions, but the alignment deteriorates when motion occurs or environmental changes happen
Solution Approach 1:
The patent implements dynamic alignment adjustment by continuously comparing IMU motion data with sensor video frame data and automatically adjusting alignment parameters in real-time. The system transitions from static calibration to dynamic adaptation, where the alignment parameters are continuously updated based on detected motion and environmental changes, resolving the contradiction between maintaining precision and adapting to changing conditions.
Solution Approach 2:
The system employs feedback mechanisms by comparing the transformed video frame data with expected motion patterns from IMU data, calculating alignment adjustment values based on this comparison, and using these feedback signals to iteratively improve alignment accuracy. This closed-loop feedback process enables the system to adapt to motion and environmental changes while maintaining high measurement precision.
2Measurement precision
If real-time alignment adjustment is implemented, then alignment accuracy under motion is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by focusing computational efforts on key alignment parameters and critical motion components rather than processing all possible variables. The system calculates alignment adjustment values based on essential IMU motion data and corresponding video frame comparisons, achieving high alignment accuracy during motion while limiting unnecessary computational complexity through selective processing of the most impactful parameters.
3Measurement precision
If frame transformation and comparison operations are performed, then alignment adjustment values are obtained, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-processing video frame data and IMU data to extract relevant features and motion parameters before the actual alignment comparison. By preparing data in advance and organizing it for efficient comparison operations, the system reduces the time required for frame transformation and comparison while maintaining high alignment precision, thus mitigating the time loss associated with these computational operations.
Data Source
AI summary
A device, system, and/or method may be used to provide an adaptive alignment. A first video data may be received. The first video data may comprise a first video frame and a second video frame. Sensor pose data may be determined. The pose data may be associated with the first video frame and the second video frame. An adjusted video frame may be determined based on the first video frame and a motion indicated by the pose data. A frame adjustment value may be determined by comparing a first pixel from the adjusted video frame to a second pixel from the second video frame. The frame adjustment value may correlate the pose data to the first video data. A second video data may be determined by applying the frame adjustment value.