用低成本传感器融合与滤波算法，实现无人机高精度实时定位定向建模
Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMUGPS Sensor Fusion and Data Filtering Algorithm
- 融合9轴IMU与GPS数据，用四元数避免方向误差
- 结合高频率加速度计与稳定但慢的GPS，实时更新位置
- 适合无人机开发测试、导航系统验证场景
无人飞行器（UAV）作为多功能平台，对精确建模的需求日益增长。本文提出一种基于数据驱动的无人机建模软件，利用成本低廉的传感器获取姿态与位置信息，并通过数据滤波算法与传感器融合技术提升数据质量，实现高精度的模型可视化。无人机姿态通过处理惯性测量单元（IMU）数据并采用四元数表示，避免万向节锁问题。位置信息由全球定位系统（GPS）提供稳定地理坐标，辅以高频率加速度计数据；由于加速度计积分易累积误差，单独使用不稳，通过融合二者数据，软件可连续准确计算并更新飞行中的实时位置。结果表明，该软件能高效呈现无人机的姿态与位置，具有高度准确性与流畅性。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity
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