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

Radu-Andrei Cioaca, Cristian Rusu, Paul Irofti, Gianluca Caparra, Andrei-Alexandru Marinache, Florin Stoican

TL;DR

A loosely coupled architecture to integrate GNSS and IMU measurements using a Factor Graph Optimization (FGO) framework is proposed and results show that the proposed approach achieves real-time operation and increased service availability compared to batch FGO methods.

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.

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

TL;DR

A loosely coupled architecture to integrate GNSS and IMU measurements using a Factor Graph Optimization (FGO) framework is proposed and results show that the proposed approach achieves real-time operation and increased service availability compared to batch FGO methods.

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.
Paper Structure (18 sections, 10 equations, 8 figures, 2 tables)

This paper contains 18 sections, 10 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Factor Graph Optimization
  4. GNSS and IMU equations
  5. Loosely coupled factor graphs
  6. State vector
  7. Factors
  8. Optimization objective
  9. Proposed method
  10. Current state-of-the-art: SFGO
  11. The proposed method: RTFGO
  12. RTFGO with smoothing latency
  13. RTFGO with marginalization lag
  14. Experimental Results
  15. The dataset
  16. ...and 3 more sections

Figures (8)

  • Figure 1: Loosely coupled GNSS/IMU integration scheme.
  • Figure 2: Loosely-coupled GNSS and IMU factor graph architecture.
  • Figure 3: Comparison of SFGO and RTFGO performance against GNSS-only and ground truth data (Loops 1 and 2)
  • Figure 4: Service availability of the proposed methods
  • Figure 5: Smoothing latency impact on the RTFGO trajectory estimated for Loop 2.
  • ...and 3 more figures