Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features

TL;DR

This paper introduces three probabilistic estimators for pose estimation from image features, evaluates their calibration and sharpness, and demonstrates their integration with Kalman filters for improved real-time autonomous landing.

Contribution

It presents novel probabilistic estimators and calibration metrics tailored for pose estimation, with comprehensive experimental comparison and application to aircraft landing systems.

Findings

Linear estimator is faster and produces sharper, well-calibrated predictions.

Estimators can be integrated with Kalman filters for improved continuous pose tracking.

50% improvement in sharpness during runway approach with marginal calibration loss.

Abstract

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 aircraft systems and provides a foundation for developing rigorous certification guidelines for such systems.