Sky-GVIO: an enhanced GNSS/INS/Vision navigation with FCN-based
  sky-segmentation in urban canyon
Jingrong Wang Bo Xu Ronghe Jin Shoujian Zhang Kefu Gao Jingnan Liu
Accurate, continuous, and reliable positioning is a critical component of
achieving autonomous driving. However, in complex urban canyon environments,
the vulnerability of a stand-alone sensor and non-line-of-sight (NLOS) caused
by high buildings, trees, and elevated structures seriously affect positioning
results. To address these challenges, a sky-view images segmentation algorithm
based on Fully Convolutional Network (FCN) is proposed for GNSS NLOS detection.
Building upon this, a novel NLOS detection and mitigation algorithm (named
S-NDM) is extended to the tightly coupled Global Navigation Satellite Systems
(GNSS), Inertial Measurement Units (IMU), and visual feature system which is
called Sky-GVIO, with the aim of achieving continuous and accurate positioning
in urban canyon environments. Furthermore, the system harmonizes Single Point
Positioning (SPP) with Real-Time Kinematic (RTK) methodologies to bolster its
operational versatility and resilience. In urban canyon environments, the
positioning performance of S-NDM algorithm proposed in this paper is evaluated
under different tightly coupled SPP-related and RTK-related models. The results
exhibit that Sky-GVIO system achieves meter-level accuracy under SPP mode and
sub-decimeter precision with RTK, surpassing the performance of GNSS/INS/Vision
frameworks devoid of S-NDM. Additionally, the sky-view image dataset, inclusive
of training and evaluation subsets, has been made publicly accessible for
scholarly exploration at https://github.com/whuwangjr/sky-view-images .