Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs

TL;DR

This paper advocates for Gaussian process-based continuous-time state estimation in robotics, offering a simplified factor graph approach and practical examples to enhance adoption and implementation.

Contribution

It introduces a simplified explanation of GP continuous-time estimation using factor graphs and provides practical examples in GTSAM to facilitate understanding.

Findings

Provides a new simplified explanation of GP continuous-time estimation.

Includes three practical examples implemented in GTSAM.

Aims to increase adoption of Gaussian process methods in robotics.

Abstract

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.