2D-to-3D Pose Estimation With Uncertainty Gating for Aerial Refueling
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Solution Overview
Problem
Existing aerial refueling techniques suffer from inaccuracies due to interference in image data acquisition, leading to inefficiencies in automated systems.
Innovation Solution
A system utilizing a camera, processor, and neural networks to predict 2D keypoints and estimate 6DOF pose through PnP optimization, combined with uncertainty analysis via Monte Carlo sampling and ensemble methods to ensure accurate 3D pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated systems use position estimation based on image data acquisition, then automation efficiency is improved, but measurement accuracy deteriorates due to interference in image data
Solution Approach 1:
The system implements feedback by calculating uncertainty scores from ensemble predictions and Monte Carlo sampling, then using these scores to determine whether to trust the automated position estimation. When uncertainty exceeds thresholds, the system requests additional images or alternative measurement methods, creating a closed-loop feedback mechanism that maintains accuracy while preserving automation efficiency.
Solution Approach 2:
The patent introduces uncertainty scoring as an intermediary mechanism between image data acquisition and position estimation. This intermediary layer evaluates the reliability of the automated estimation by comparing ensemble predictions and Monte Carlo samples, allowing the system to mediate between automated efficiency and measurement accuracy by triggering re-acquisition only when necessary.
2Reliability
If ensemble of neural networks is used for keypoint prediction, then reliability is improved, but device complexity increases
Solution Approach 1:
The system applies partial action by using Monte Carlo sampling to generate only a subset of possible keypoint configurations rather than exhaustively evaluating all possibilities. The ensemble of neural networks provides multiple predictions, but Monte Carlo sampling selectively explores the solution space, achieving reliable position estimation without the computational burden of complete enumeration.
Solution Approach 2:
The patent changes parameters by varying the number of Monte Carlo samples and ensemble size dynamically. The system adjusts these parameters based on computational resources available and the required precision level, allowing flexibility in balancing reliability against device complexity. Different operational contexts can use different parameter settings to optimize the trade-off.
Data Source
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.