  This article proposes a visual inertial navigation algorithm intended to
diminish the horizontal position drift experienced by autonomous fixed wing
UAVs (Unmanned Air Vehicles) in the absence of GNSS (Global Navigation
Satellite System) signals. In addition to accelerometers, gyroscopes, and
magnetometers, the proposed navigation filter relies on the accurate
incremental displacement outputs generated by a VO (Visual Odometry) system,
denoted here as a Virtual Vision Sensor or VVS, which relies on images of the
Earth surface taken by an onboard camera and is itself assisted by the filter
inertial estimations. Although not a full replacement for a GNSS receiver since
its position observations are relative instead of absolute, the proposed system
enables major reductions in the GNSS-Denied attitude and position estimation
errors. In order to minimize the accumulation of errors in the absence of
absolute observations, the filter is implemented in the manifold of rigid body
rotations or SO (3). Stochastic high fidelity simulations of two representative
scenarios involving the loss of GNSS signals are employed to evaluate the
results. The authors release the C++ implementation of both the visual inertial
navigation filter and the high fidelity simulation as open-source software.
