摘要
提出了一种基于扩展卡尔曼滤波的惯性导航系统(INS)/光流/磁强计/气压计组合导航方案。将INS、光流和气压计数据融合,估计无人机(UAV)的速度和位置。当UAV静止或匀速运动时,将陀螺仪与加速计、磁强计、光流的数据融合,估计UAV的姿态。当UAV加速或减速时,用陀螺仪估计UAV的姿态。实验结果表明,UAV的速度误差从最大20m/s减小到了10m/s,角度误差从最大80°减小到了10°。该方案可以有效解决速度、位置和姿态估计的累积误差问题。
One navigation scheme by integrating inertial navigation system （INS）/optical flow/magnetometer/ barometer is proposed based on the extended Kalman filtering. Through data fusion of INS, optical flow and barometer, the speed and position of unmanned aerial vehicle （UAV） are estimated. When UAV is at rest or in motion with constant velocity, the UAV attitude is estimated by means of data fusion of gyro, accelerometer, magnetometer, and optical flow. When UAV is accelerated or decelerated, the gyroscope data is used to estimate the UAV attitude. The experimental results show that, the speed error for UAV is reduced from the maximum 20 m/s to 10 m/s, and the angle error is reduced from the maximum 80° to 10°. This scheme can effectively solve the cumulative error problem of velocity, position and attitude estimation.
出处
《激光与光电子学进展》
CSCD
北大核心
2017年第2期291-299,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61603353)
关键词
遥感
无人机
惯性导航
光流传感器
磁强计
气压计
扩展卡尔曼滤波
remote sensing
unmanned aerial vehicle
inertial navigation
optical flow sensor
magnetometer
barometer
extended Kalman filtering
