  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.
