周璠, 郑伟, 汪增福. 基于多异类传感器信息融合的微型多旋翼无人机实时运动估计[J]. 机器人, 2015, 37(1): 94-101. DOI: 10.13973/j.cnki.robot.2015.094
引用本文: 周璠, 郑伟, 汪增福. 基于多异类传感器信息融合的微型多旋翼无人机实时运动估计[J]. 机器人, 2015, 37(1): 94-101. DOI: 10.13973/j.cnki.robot.2015.094
ZHOU Fan, ZHENG Wei, WANG Zengfu. Real-time Motion Estimation for UAVs Based on Dissimilar Multi-sensor Data Fusion[J]. ROBOT, 2015, 37(1): 94-101. DOI: 10.13973/j.cnki.robot.2015.094
Citation: ZHOU Fan, ZHENG Wei, WANG Zengfu. Real-time Motion Estimation for UAVs Based on Dissimilar Multi-sensor Data Fusion[J]. ROBOT, 2015, 37(1): 94-101. DOI: 10.13973/j.cnki.robot.2015.094

基于多异类传感器信息融合的微型多旋翼无人机实时运动估计

Real-time Motion Estimation for UAVs Based on Dissimilar Multi-sensor Data Fusion

  • 摘要: 针对无人飞行器视觉定位结果存在较大时延而影响飞行器运动状态估计精度的问题,提出了一种基于多传感器数据融合的实时运动估计方法.首先,利用机载惯性测量元件(IMU)提供的姿态信息优化单目视觉定位算法,使得视觉定位结果的时延减小.然后,在利用卡尔曼滤波器估计飞行器运动状态的过程中,考虑了视觉定位结果的时延,利用加速度信息进行时延补偿.最终得到实时的高精度运动估计结果.在自主研制的四旋翼飞行器系统上对本文提出的方法进行了验证.通过与不考虑时延的方法的结果以及真实数据进行比较,证明了本方法的有效性.

     

    Abstract: Large time-delay usually exists in the visual localization results of unmanned aerial vehicles (UAVs), which lowers the accuracy of motion estimation. To solve this problem, a real-time motion estimation method based on multi-sensor data fusion is proposed. Firstly, attitude information provided by the inertial measurement unit (IMU) is fused into the monocular visual localization algorithm, so that the delay in localization results can be reduced. Then, the delay of the visual localization results is considered during the motion estimation using Kalman filter, and the measurements of the accelerometer are used to compensate the delay. With the proposed method, motion estimation results with high accuracy are obtained in real time. The method is validated by field experiments on a quadrotor system. By comparing the motion estimation results with the method without delay compensation as well as the ground truth data, the effectiveness of the proposed method is verified.

     

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