リー群上の不変スムージング
Invariant Smoothing on Lie Groups
リー群上の群アフィン観測系に対し、線形化が現在の推定値に依存しない性質を利用して再線形化を削減する非線形スムージング法を提案し、ロボット自己位置推定で有効性を示した。
著者: Paul Chauchat, Axel Barrau, Silvère Bonnabel
分類: cs.RO
原文アブストラクト
In this paper we propose a (non-linear) smoothing algorithm for group-affine observation systems, a recently introduced class of estimation problems on Lie groups that bear a particular structure. As most non-linear smoothing methods, the proposed algorithm is based on a maximum a posteriori estimator, determined by optimization. But owing to the specific properties of the considered class of problems, the involved linearizations are proved to have a form of independence with respect to the current estimates, leveraged to avoid (partially or sometimes totally) the need to relinearize. The method is validated on a robot localization example, both in simulations and on real experimental data.
