用因子图框架简化高斯过程连续时间估计，让机器人定位更平滑准确。
Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs
- 基于因子图语言重构高斯过程连续时间估计方法
- 在异步传感器下实现平滑状态估计，提升定位精度
- 提供三个GTSAM实现示例，降低上手门槛
连续时间状态估计因能生成平滑解、处理异步传感器并插值数据点而日益流行。尽管存在参数化（如时间基函数、样条）和非参数化（高斯过程）两大范式，后者虽技术优势明显且实现简便，但应用仍较少。本文通过因子图语言重新解释高斯过程连续时间估计，使该方法更易被机器人领域接受。为降低入门难度，我们还提供了三个基于流行估计框架GTSAM的可运行示例。
原文摘要 · Abstract (English)
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.
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