电光与控制, 2019, 26 (1): 97, 网络出版: 2021-01-13   

面向弱光流环境的惯性/光流组合导航方法研究

On Inertial/Optical Flow Combined Navigation Method for Weak Optical Flow
作者单位
太原工业学院电子工程系, 太原 030008
摘要
惯性导航系统(INS)与光流组合导航方法在众多场合拥有极为广泛的应用, 其中, 光流信息的准确与否直接影响导航参数的优劣。为解决光照极弱或者光流传感器离地高度小于摄像头焦距所导致的光流信息误差较大使导航参数严重失真而无法连续导航的问题, 提出一种基于Elman神经网络的速度预测方法。环境适宜的情况下, 在线训练神经网络模型, 而处于特殊环境使光流信息信任价值很小时, 使用训练完成的神经网络对载体速度进行预测。另外, 基于INS动态误差模型的卡尔曼滤波器(KF)通过融合INS以及速度数据得到误差向量使之对导航参数进行补偿修正。小型四轴飞行器飞行试验表明, 神经网络的预测值能够在较短时间内高精度地逼近真实值, 证明了上述算法的正确性和有效性。通过与真实值相比较, 平均姿态误差为0.1%, 平均速度误差为1%, 平均位置误差为2.4%。
Abstract
The integrated INS/optical flow navigation method has found wide applications in many occasions.The accuracy of optical flow has direct influence on the performance of navigation parameters. When the optical flow has great error due to low illumination or other reasons, the accuracy of navigation parameters will decline greatly and thus cannot continue to navigate.To overcome this problem, a method for velocity forecasting based on Elman neural network is proposed.When the situation is suitable, the neural network model is trained online;and under special condition when the reliability of optical flow is very small, the neural network model that has already been trained is used for forecasting the velocity of the platform.In addition, the KF based on the INS dynamic error model can obtain the error vector by fusing the data of the INS with the optical flow, which is then used for correcting the navigation parameters. The trial based on the small quad-rotor air vehicle shows that, the forecasting of velocity from the neural network can closely approximate the true value in a short time, which demonstrates the correctness and effectiveness of the method. By comparing with the real value, the average error of attitude, velocity and position are 0.1%, 1%, and 2.4% respectively.

王瑞荣, 陈瞳, 李晓红. 面向弱光流环境的惯性/光流组合导航方法研究[J]. 电光与控制, 2019, 26(1): 97. WANG Rirong, CHEN Tong, LI Xiaohong. On Inertial/Optical Flow Combined Navigation Method for Weak Optical Flow[J]. Electronics Optics & Control, 2019, 26(1): 97.

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