改进视觉惯性里程计融合GPS的无人机定位方法研究

为提升无人机大范围弱纹理场景下的状态估计，提出一种改进视觉惯性里程计融合GPS的定位方法。首先，通过在视觉惯性里程计中加入线特征来表示环境的几何结构信息，提升位姿估计的准确性；其次，通过引入长度阈值筛选，剔除对位姿估计贡献不大的短线段，改善特征追踪的鲁棒性；最后，使用非线性优化的方式，将GPS测量信息和改进的视觉惯性里程计融合，校正视觉惯性里程计的累积误差。基于EuRoC数据集仿真实验以及应用于无人机的真实场景实验表明，相较于原算法，加入线特征算法的定位误差在仿真实验中降低了39.14%，室内场景降低了23.48%，室外场景降低了33.58%。融合了GPS的点线特征算法相较于原算法，定位误差降低了53.99%。

Research on UAV localization method based on improved visual inertial odometry and GPS

In order to improve the state estimation of UAV in a large range of weak texture scenes, an improved visual inertial odometer combined with GPS positioning method is proposed. Firstly, the geometric structure information of the environment was represented by adding line features into the visual inertial odometer to improve the accuracy of pose estimation. Secondly, by introducing length threshold screening, the short line segments that do not contribute much to pose estimation are eliminated to improve the robustness of feature tracking. Finally, the GPS measurement information is fused with the improved visual inertial odometer in a nonlinear optimization way to correct the cumulative error of the visual inertial odometer. The simulation experiment based on EuRoC dataset and the real scene experiment applied to UAV show that, compared with the original algorithm, the positioning error of the line feature algorithm is reduced by 39.14% in the simulation experiment, 23.48% in the indoor scene and 33.58% in the outdoor scene. The point and line feature algorithm integrated with GPS. The positioning error was reduced by 53.99%.