Robust Navigation In GNSS Degraded Environment Using Graph Optimization

TL;DR

This paper explores the use of graph optimization and robust sensor fusion techniques to improve GNSS-based navigation in urban environments where satellite signals are degraded.

Contribution

It introduces a novel application of factor graph methods and robust optimization techniques to enhance GNSS data processing in challenging urban settings.

Findings

Robust optimization improves GNSS positioning accuracy in urban environments.

The study demonstrates the effectiveness of graph-based sensor fusion for degraded GNSS signals.

Open-source software and datasets are provided for further research.

Abstract

Robust navigation in urban environments has received a considerable amount of both academic and commercial interest over recent years. This is primarily due to large commercial organizations such as Google and Uber stepping into the autonomous navigation market. Most of this research has shied away from Global Navigation Satellite System (GNSS) based navigation. The aversion to utilizing GNSS data is due to the degraded nature of the data in urban environment (e.g., multipath, poor satellite visibility). The degradation of the GNSS data in urban environments makes it such that traditional (GNSS) positioning methods (e.g., extended Kalman filter, particle filters) perform poorly. However, recent advances in robust graph theoretic based sensor fusion methods, primarily applied to Simultaneous Localization and Mapping (SLAM) based robotic applications, can also be applied to GNSS data processing. This paper will utilize one such method known as the factor graph in conjunction several robust optimization techniques to evaluate their applicability to robust GNSS data processing. The goals of this study are two-fold. First, for GNSS applications, we will experimentally evaluate the effectiveness of robust optimization techniques within a graph-theoretic estimation framework. Second, by releasing the software developed and data sets used for this study, we will introduce a new open-source front-end to the Georgia Tech Smoothing and Mapping (GTSAM) library for the purpose of integrating GNSS pseudorange observations.