# The Syncline Model -- Analyzing the Impact of Time Synchronization in Sensor Fusion

arXiv: https://arxiv.org/abs/2209.01136 · HTML: https://mlsanity.com/paper/2209.01136/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies](https://mlsanity.com/paper/2603.16952/) (2026)
- [Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation](https://mlsanity.com/paper/2309.14932/) (2023)
- [Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures](https://mlsanity.com/paper/2601.23147/) (2026)

## Abstract (arXiv)

The accuracy of sensor fusion algorithms are limited by either the intrinsic sensor noise, or by the quality of time synchronization of the sensors. While the intrinsic sensor noise only depends on the respective sensors, the error induced by quality of, or lack of, synchronization depends on the dynamics of the vehicles and robotic system and the magnitude of time synchronization errors. To meet their sensor fusion requirements, system designers must consider both which sensor to use and also how to synchronize them. This paper presents the Syncline model, a simple visual model of how time synchronization affects the accuracy of sensor fusion for different mobile robot platform. The model can serve as a simple tool to determine which synchronization mechanisms should be used.