Real-time loosely coupled GNSS and IMU integration via Factor Graph Optimization

TL;DR

This paper presents a real-time, loosely coupled GNSS and IMU integration method using Factor Graph Optimization, balancing accuracy, availability, and computational efficiency for urban navigation.

Contribution

It introduces a real-time FGO-based GNSS/IMU fusion architecture optimized for challenging environments, with a detailed analysis of trade-offs involved.

Findings

Achieves real-time operation in urban environments.

Increases service availability over batch FGO methods.

Trade-offs between accuracy and computational efficiency are characterized.

Abstract

Accurate positioning, navigation, and timing (PNT) is fundamental to the operation of modern technologies and a key enabler of autonomous systems. A very important component of PNT is the Global Navigation Satellite System (GNSS) which ensures outdoor positioning. Modern research directions have pushed the performance of GNSS localization to new heights by fusing GNSS measurements with other sensory information, mainly measurements from Inertial Measurement Units (IMU). In this paper, we propose a loosely coupled architecture to integrate GNSS and IMU measurements using a Factor Graph Optimization (FGO) framework. Because the FGO method can be computationally challenging and often used as a post-processing method, our focus is on assessing its localization accuracy and service availability while operating in real-time in challenging environments (urban canyons). Experimental results on the UrbanNav-HK-MediumUrban-1 dataset show that the proposed approach achieves real-time operation and increased service availability compared to batch FGO methods. While this improvement comes at the cost of reduced positioning accuracy, the paper provides a detailed analysis of the trade-offs between accuracy, availability, and computational efficiency that characterize real-time FGO-based GNSS/IMU fusion.