6DOF Pose Uncertainty Estimation for 2D-to-3D Image Prediction
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Solution Overview
Problem
Existing aerial refueling techniques suffer from inaccuracies in position estimation due to interference with image acquisition, leading to inefficiencies and safety concerns.
Innovation Solution
A system utilizing a camera, processor, and neural networks to predict 2D keypoints and estimate 6DOF pose with PnP optimization, combined with uncertainty analysis from ensemble and Monte Carlo sampling to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional position estimation methods are used in aerial refueling, then the system complexity remains low, but measurement precision and reliability deteriorate due to image acquisition interference
Solution Approach 1:
The patent introduces an intermediary uncertainty estimation module that acts as a mediator between the image acquisition system and the position estimation system. This module processes the output of neural networks and provides uncertainty scores that indicate the reliability of position estimates, allowing the system to account for image acquisition interference without fundamentally changing the core estimation architecture.
Solution Approach 2:
The patent implements feedback mechanisms where uncertainty scores from ensemble neural networks and Monte Carlo sampling are fed back into the position estimation system. This feedback allows the system to adjust its confidence in position estimates based on the quality of input images and the consistency of neural network outputs, improving measurement precision while maintaining manageable system complexity through structured information flow.
2Reliability
If ensemble neural networks and Monte Carlo sampling are used to reduce uncertainty, then reliability improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple neural networks in an ensemble before deployment. These networks are trained offline on diverse datasets that account for various image acquisition conditions, allowing the system to reliably estimate uncertainty during operation without requiring complex real-time processing. The preliminary training phase captures the complexity, while the operational phase benefits from the prepared models.
Solution Approach 2:
The patent uses partial action by implementing uncertainty estimation only for critical position parameters that require high reliability, such as the relative position between tanker and receiver aircraft. Not all position estimates require the full computational overhead of ensemble methods and Monte Carlo sampling, allowing the system to balance reliability improvements with computational constraints by applying these techniques selectively.
3Measurement precision
If multiple uncertainty sources are analyzed, then measurement precision improves, but loss of time increases due to additional processing
Solution Approach 1:
The patent segments the uncertainty analysis into distinct independent components: uncertainty from ensemble neural network variations and uncertainty from Monte Carlo sampling of the PnP problem. Each component is calculated separately using optimized algorithms, and the results are combined to form the total uncertainty score. This segmentation allows each component to be processed efficiently without the overhead of a monolithic uncertainty calculation, reducing overall processing time while maintaining comprehensive uncertainty analysis.
Data Source
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AI summary
A method includes receiving a two-dimensional (2D) image from a camera, predicting 2D keypoints of a target object within the 2D image based on a previously trained ensemble of neural networks, and estimating 6 degree-of-freedom (6DOF) position (pose) of the target object using the 2D keypoints using a perspective-n-point (PnP) optimization technique to create 6DOF pose parameters for each neural network in the ensemble. The method combines the result into a single estimate of 6DOF pose parameters. The method also includes determining an uncertainty score based on a first uncertainty value and a second uncertainty value, and outputting the 6DOF pose parameters in response to the uncertainty score being within a predefined threshold.