エッジを滑らかに：因子グラフ上のガウス過程運動事前分布による連続時間推定
Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs
因子グラフの枠組みでガウス過程による連続時間状態推定を簡潔に解説し、GTSAMでの実装例を提供する論文。
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著者: Connor Holmes, Sven Lilge, Zi Cong Guo, Frank Dellaert, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
Continuous-time state estimation is gaining in popularity due to its abilities to provide smooth solutions, handle asynchronous sensors, and interpolate between data points. While there are two main paradigms, parametric (e.g., temporal basis functions, splines) and nonparametric (Gaussian processes), the latter has seen less adoption despite its technical advantages and relative ease of implementation. In this article, we seek to rectify this situation by providing a new simplified explanation of GP continuous-time estimation rooted in the language of factor graphs, which have become the de facto estimation paradigm in much of robotics. To simplify onboarding, we also provide three working examples implemented in the popular GTSAM estimation framework.
