Accurate navigation is essential for autonomous robots and vehicles. In
recent years, the integration of the Global Navigation Satellite System (GNSS),
Inertial Navigation System (INS), and camera has garnered considerable
attention due to its robustness and high accuracy in diverse environments. In
such systems, fully utilizing the role of GNSS is cumbersome because of the
diverse choices of formulations, error models, satellite constellations, signal
frequencies, and service types, which lead to different precision, robustness,
and usage dependencies. To clarify the capacity of GNSS algorithms and
accelerate the development efficiency of employing GNSS in multi-sensor fusion
algorithms, we open source the GNSS/INS/Camera Integration Library (GICI-LIB),
together with detailed documentation and a comprehensive land vehicle dataset.
A factor graph optimization-based multi-sensor fusion framework is established,
which combines almost all GNSS measurement error sources by fully considering
temporal and spatial correlations between measurements. The graph structure is
designed for flexibility, making it easy to form any kind of integration
algorithm. For illustration, four Real-Time Kinematic (RTK)-based algorithms
from GICI-LIB are evaluated using our dataset. Results confirm the potential of
the GICI system to provide continuous precise navigation solutions in a wide
spectrum of urban environments.Comment: Open-source: https://github.com/chichengcn/gici-open. This work has
been submitted to the IEEE for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
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