時間相関測定ノイズモデルによる一貫性のあるバッチ状態推定
Towards Consistent Batch State Estimation Using a Time-Correlated Measurement Noise Model
測定ノイズの時間相関を考慮したバッチ状態推定のための共分散学習アルゴリズムを提案し、移動ロボットの実験データで推定精度と一貫性の向上を示した。
著者: David J. Yoon, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
In this paper, we present an algorithm for learning time-correlated measurement covariances for application in batch state estimation. We parameterize the inverse measurement covariance matrix to be block-banded, which conveniently factorizes and results in a computationally efficient approach for correlating measurements across the entire trajectory. We train our covariance model through supervised learning using the groundtruth trajectory. In applications where the measurements are time-correlated, we demonstrate improved performance in both the mean posterior estimate and the covariance (i.e., improved estimator consistency). We use an experimental dataset collected using a mobile robot equipped with a laser rangefinder to demonstrate the improvement in performance. We also verify estimator consistency in a controlled simulation using a statistical test over several trials.
