951 research outputs found
InGVIO: A Consistent Invariant Filter for Fast and High-Accuracy GNSS-Visual-Inertial Odometry
Combining Global Navigation Satellite System (GNSS) with visual and inertial
sensors can give smooth pose estimation without drifting in geographical
coordinates. The fusion system gradually degrades to Visual-Inertial Odometry
(VIO) with the number of satellites decreasing, which guarantees robust global
navigation in GNSS unfriendly environments. In this letter, we propose an
open-sourced invariant filter-based platform, InGVIO, to tightly fuse
monocular/stereo visual-inertial measurements, along with raw data from GNSS,
i.e. pseudo ranges and Doppler shifts. InGVIO gives highly competitive results
in terms of accuracy and computational load compared to current graph-based and
`naive' EKF-based algorithms. Thanks to our proposed key-frame marginalization
strategies, the baseline for triangulation is large although only a few cloned
poses are kept. Besides, landmarks are anchored to a single cloned pose to fit
the nonlinear log-error form of the invariant filter while achieving decoupled
propagation with IMU states. Moreover, we exploit the infinitesimal symmetries
of the system, which gives equivalent results for the pattern of degenerate
motions and the structure of unobservable subspaces compared to our previous
work using observability analysis. We show that the properly-chosen invariant
error captures such symmetries and has intrinsic consistency properties. InGVIO
is tested on both open datasets and our proposed fixed-wing datasets with
variable levels of difficulty. The latter, to the best of our knowledge, are
the first datasets open-sourced to the community on a fixed-wing aircraft with
raw GNSS.Comment: 8 pages, 8 figures; manuscript will be submitted to IEEE RA-L for
possible publicatio
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