学習可能なスムーザのための微分可能な因子グラフ最適化
Differentiable Factor Graph Optimization for Learning Smoothers
因子グラフベースのスムーザを微分可能な形で展開し、MAP推論の最適化を通じて確率的システムモデルをエンドツーエンドで学習する手法を提案。物体追跡と視覚オドメトリで既存手法を大きく上回る精度を示した。
著者: Brent Yi, Michelle A. Lee, Alina Kloss, Roberto Martín-Martín, Jeannette Bohg
分類: cs.RO
原文アブストラクト
A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-design or tune, while retaining the core advantages of probabilistic state estimation. As an alternative approach for state estimation in these settings, we present an end-to-end approach for learning state estimators modeled as factor graph-based smoothers. By unrolling the optimizer we use for maximum a posteriori inference in these probabilistic graphical models, we can learn probabilistic system models in the full context of an overall state estimator, while also taking advantage of the distinct accuracy and runtime advantages that smoothers offer over recursive filters. We study this approach using two fundamental state estimation problems, object tracking and visual odometry, where we demonstrate a significant improvement over existing baselines. Our work comes with an extensive code release, which includes training and evaluation scripts, as well as Python libraries for Lie theory and factor graph optimization: https://sites.google.com/view/diffsmoothing/
