多传感器数据融合的无人机速率估算与定位

为了提高农用无人机速率与位置的估算精度,同时降低无人机制作成本,提出了一种对于农用无人机容易实现的传感器数据融合算法,即通过使用离散型卡尔曼滤波,提高实验对象在三维空间中位置和速率变化的估算精度。由于传统的惯性测量系统存在体积大、造价高等缺点,而廉价的惯性测量传感器又存在较大的飘移,因此结合农用无人机航拍的工作环境采用全球定位系统(GPS)提供位置的测量,由惯性导航系统(INS)给出加速度,并由光流传感器提供速度的测量加以辅助。最终,通过实验验证了该算法的有效性。

Data Fusion for Velocity and Position Estimation of UAV

In order to improve the estimation accuracy of UAV speed and position , and reduce the production cost of UAV,put fusion algorithm ,by using the discrete Kalman filter ,improve the accuracy of estimating changes of the position of the object in three-dimensional space and speed .Due to the disadvantages of large volume ,high cost of the traditional inertial measurement system ,and low-cost inertial measurement sensors have bigger drift ,this combination of UAV aerial work environment using the global positioning system ( GPS ) provides the position measurement , by inertial navigation system ( INS) is given by the acceleration ,and optical flow sensor speed the measurement of auxiliary .The results show that the proposed algorithm .