画像特徴からの姿勢推定における確率的パラメータ推定器とキャリブレーション指標
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features
画像特徴からの姿勢推定において、測定不確かさを考慮した3つの確率的推定手法を比較し、多変量正規分布向けの新たなキャリブレーション評価指標を提案した。線形近似法が高速で良好な予測を実現し、カルマンフィルタと統合することで連続姿勢推定の精度が向上することを示した。
著者: Romeo Valentin, Sydney M. Katz, Joonghyun Lee, Don Walker, Matthew Sorgenfrei, Mykel J. Kochenderfer
分類: cs.RO, cs.CV
原文アブストラクト
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.
