基于视惯融合的无人飞行器导航数据仿真方法

无人飞行器在卫星信号拒止情况下难以获得较高的导航精度。为此，需要研究基于载体自身传感设备的自主导航定位方法。立足于飞行器平台的视觉惯性紧耦合算法架构VINS-Mono，提出并设计了一种视觉惯性数据仿真方法，针对匀速直线运动过程无法恢复视觉尺度的问题，进行了初始化运动轨迹补充。仿真研究结果表明，该方法所补充的轨迹能够很好地对齐视觉与惯性信息并恢复尺度，实现导航参数的正确求解。理想条件下500m平飞范围内定位误差占比不超过真值的3.5%，可作为评估卫星拒止环境下无人飞行器视觉惯性组合导航性能的参考方案。

Data Simulation Method of UAV Navigation on Visual-Inertial Fusion

It is hard for UAVs to obtain high navigation accuracy under the condition of satellite signal rejection. Therefore, studying the autonomous navigation and positioning method with sensing equipment is significant. Based on the tightly coupled visual inertia algorithm architecture VINS-Mono on aircraft platforms, a visual-inertial data simulation method is proposed in this paper. The initial motion trajectory is supplemented for solving problems that the visual scale cannot be restored in uniform linear motion. The simulation experiment results show that the navigation parameters and the scale can be correctly solved by adding the trajectory for the alignment of visual and inertial information. Under ideal conditions, the positioning error within 500m is less than 3.5% of the true value, which provides an important algorithm reference for UAV visual-inertial integrated navigation in this scene.