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This article proposes a visual inertial navigation algorithm intended todiminish the horizontal position drift experienced by autonomous fixed wingUAVs (Unmanned Air Vehicles) in the absence of GNSS (Global NavigationSatellite System) signals. In addition to accelerometers, gyroscopes, andmagnetometers, the proposed navigation filter relies on the accurateincremental displacement outputs generated by a VO (Visual Odometry) system,denoted here as a Virtual Vision Sensor or VVS, which relies on images of theEarth surface taken by an onboard camera and is itself assisted by the filterinertial estimations. Although not a full replacement for a GNSS receiver sinceits position observations are relative instead of absolute, the proposed systemenables major reductions in the GNSS-Denied attitude and position estimationerrors. In order to minimize the accumulation of errors in the absence ofabsolute observations, the filter is implemented in the manifold of rigid bodyrotations or SO (3). Stochastic high fidelity simulations of two representativescenarios involving the loss of GNSS signals are employed to evaluate theresults. The authors release the C++ implementation of both the visual inertialnavigation filter and the high fidelity simulation as open-source software.

aerospace

Long Distance GNSS-Denied Visual Inertial Navigation for Autonomous  Fixed Wing Unmanned Air Vehicles: SO(3) Manifold Filter based on Virtual  Vision Sensor