UniMSF: A Unified Multi-Sensor Fusion Framework for Intelligent Transportation System Global Localization

TL;DR

UniMSF is a comprehensive multi-sensor fusion framework for ITS localization that combines GNSS, 4D-radar, and IMU data using factor graphs, ensuring accurate, reliable, and modular localization even with measurement heterogeneity and outliers.

Contribution

The paper introduces UniMSF, a novel, modular multi-sensor fusion framework utilizing factor graphs, with advanced outlier detection and noise estimation for ITS localization.

Findings

Effective fusion of GNSS, radar, and IMU demonstrated in real vehicle tests.

Robust outlier detection improves localization accuracy.

Framework adaptable to various sensor configurations.

Abstract

Intelligent transportation systems (ITS) localization is of significant importance as it provides fundamental position and orientation for autonomous operations like intelligent vehicles. Integrating diverse and complementary sensors such as global navigation satellite system (GNSS) and 4D-radar can provide scalable and reliable global localization. Nevertheless, multi-sensor fusion encounters challenges including heterogeneity and time-varying uncertainty in measurements. Consequently, developing a reliable and unified multi-sensor framework remains challenging. In this paper, we introduce UniMSF, a comprehensive multi-sensor fusion localization framework for ITS, utilizing factor graphs. By integrating a multi-sensor fusion front-end, alongside outlier detection\&noise model estimation, and a factor graph optimization back-end, this framework accomplishes efficient fusion and ensures accurate localization for ITS. Specifically, in the multi-sensor fusion front-end module, we tackle the measurement heterogeneity among different modality sensors and establish effective measurement models. Reliable outlier detection and data-driven online noise estimation methods ensure that back-end optimization is immune to interference from outlier measurements. In addition, integrating multi-sensor observations via factor graph optimization offers the advantage of \enquote{plug and play}. Notably, our framework features high modularity and is seamlessly adapted to various sensor configurations. We demonstrate the effectiveness of the proposed framework through real vehicle tests by tightly integrating GNSS pseudorange and carrier phase information with IMU, and 4D-radar.