用于自主固定翼无人机的长距离拒止GNSS视觉惯性导航：基于虚拟视觉传感器的SO(3)流形滤波器
Long Distance GNSS-Denied Visual Inertial Navigation for Autonomous Fixed Wing Unmanned Air Vehicles: SO(3) Manifold Filter based on Virtual Vision Sensor
AI总结：
针对固定翼无人机在GNSS拒止环境下的长距离导航漂移问题，提出融合VO/VVS相对位移与IMU、磁力计并在SO(3)流形上实现的视觉惯性滤波方法，经高保真仿真验证并开源。
摘要：
本文提出了一种视觉惯性导航算法，旨在减轻自主固定翼UAV（无人机）在缺失GNSS（全球导航卫星系统）信号时出现的水平位置漂移。除加速度计、陀螺仪和磁力计之外，所提出的导航滤波器还依赖于由VO（视觉里程计）系统生成的精确增量位移输出；该系统在本文中称为虚拟视觉传感器（Virtual Vision Sensor，VVS），它利用机载相机拍摄的地球表面图像，并且其本身由滤波器的惯性估计提供辅助。尽管由于其位置观测是相对的而非绝对的，该系统不能完全替代GNSS接收机，但它能够大幅降低拒止GNSS条件下的姿态和位置估计误差。为了在没有绝对观测的情况下最大限度地减少误差累积，该滤波器在刚体旋转流形（即SO(3)）上实现。研究采用两个涉及GNSS信号丢失的代表性场景的随机高保真仿真来评估结果。作者以开源软件形式发布了视觉惯性导航滤波器和高保真仿真的C++实现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
