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.

翻译：本文提出一种视觉惯性导航算法，旨在减少自主固定翼无人机在无全球导航卫星系统（GNSS）信号时的水平位置漂移。该导航滤波器除依靠加速度计、陀螺仪和磁力计外，还依赖于视觉里程计（VO）系统生成的精确增量位移输出（本文称为虚拟视觉传感器或VVS），该系统利用机载相机拍摄的地球表面图像，并借助滤波器的惯性估计结果提供辅助。尽管由于位置观测为相对量而非绝对量，该系统无法完全替代GNSS接收机，但能显著降低GNSS拒止环境下的姿态与位置估计误差。为最大限度减少无绝对观测时的误差累积，滤波器基于刚体旋转流形SO(3)实现。通过模拟两种典型GNSS信号丢失场景的高保真随机仿真，对算法结果进行了评估。作者已将视觉惯性导航滤波器及高保真仿真的C++实现作为开源软件发布。