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Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features by Valentin, Romeo; Katz, Sydney M.; Lee, Joonghyun; Walker, Don; Sorgenfrei, Matthew; Kochenderfer, Mykel J. is a scholarly article available to read on EtoBox.

What is Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features about?

This paper addresses the challenge of probabilistic parameter estimation given measurement uncertainty in real-time. We provide a general formulation and apply this to pose estimation for an autonomous visual landing system. We present three probabilistic parameter estimators: a least-squares sampling approach, a linear approximation method, and a probabilistic programming estimator. To evaluate these estimators, we introduce novel closed-form expressions for measuring calibration and sharpness specifically for multivariate normal distributions. Our experimental study compares the three estimators under various noise conditions. We demonstrate that the linear approximation estimator can produce sharp and well-calibrated pose predictions significantly faster than the other methods but may yield overconfident predictions in certain scenarios. Additionally, we demonstrate that these estimators can be integrated with a Kalman filter for continuous pose estimation during a runway approach where we observe a 50\% improvement in sharpness while maintaining marginal calibration. This work contributes to the integration of data-driven computer vision models into complex safety-critical airc

Author

Valentin, Romeo; Katz, Sydney M.; Lee, Joonghyun; Walker, Don; Sorgenfrei, Matthew; Kochenderfer, Mykel J.

Published

2024

Language

EN