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

2026-05-09

AI Summary

This work addresses the limited adoption of Gaussian processes (GPs) in continuous-time state estimation—primarily hindered by their high theoretical barrier—by introducing a GP modeling approach formulated within a factor graph framework. By re-expressing the GP motion prior using factor graph semantics, the proposed method naturally supports asynchronous multi-sensor fusion and trajectory interpolation while yielding smooth, continuous trajectories. The authors provide three open-source implementations built on GTSAM, significantly lowering the practical entry barrier for employing GP-based continuous-time estimation and thereby facilitating its real-world deployment and application in robotic systems.

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.

Problem

• continuous-time estimation
• Gaussian processes
• factor graphs
• state estimation
• robotics

Innovation

• Gaussian Process
• continuous-time estimation
• factor graphs
• state estimation
• GTSAM