Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM

2026-06-04

AI Summary

This work addresses the challenges of fusing and synchronizing heterogeneous asynchronous sensors—such as rolling-shutter cameras, LiDAR, and event cameras—in continuous-time SLAM. The authors propose G-solver, a novel framework that uniquely integrates Gaussian belief propagation with a data-driven Gaussian process motion prior to enable accurate trajectory estimation without requiring hardware synchronization. At its core, G-solver models the trajectory continuously via a Gaussian process and leverages distributed Gaussian belief propagation for efficient inference, supporting temporal interpolation across sensor modalities and automatic hyperparameter learning. Experiments demonstrate that G-solver achieves accuracy and efficiency on par with state-of-the-art continuous-time SLAM methods on both synthetic and real-world datasets, while inherently supporting distributed optimization. The implementation is publicly available.

Abstract

Continuous-time SLAM provides a principled framework for fusing heterogeneous sensors while estimating smooth trajectories, and is particularly well-suited for handling heterogeneous, asynchronous sensor streams with non-uniform readout patterns, such as rolling shutter cameras, LiDAR scanners, radar sweeps, or event-based sensors. In this work, we introduce G-solver, a fully Gaussian and distributed framework that combines Gaussian Belief Propagation (GBP) with Gaussian Process (GP) motion priors for continuous-time trajectory estimation. Our GP model provides a probabilistic representation of the trajectory, enabling consistent interpolation and the use of data-driven hyperparameters, while GBP offers a scalable message-passing formulation well-suited for decentralized settings. The resulting solver naturally extends to multi-camera scenarios without specialized synchronization or engineering effort. We evaluate the approach on synthetic and real data, including rolling shutter and distributed multi-camera optimization, demonstrating accurate and stable estimation with runtimes comparable to existing continuous-time methods. An open-source implementation is released.

Problem

• continuous-time SLAM
• heterogeneous sensors
• distributed estimation
• trajectory estimation
• asynchronous sensor fusion

Innovation

• Gaussian Belief Propagation
• Gaussian Process
• Continuous-time SLAM
• Distributed Optimization
• Rolling Shutter