一种复杂多介质环境下的视觉/惯性自适应组合导航方法  

Adaptive integrated navigation method of visual positioning/INS in complex multi-medium environment

作　　者：张朝飞[1,2] 罗建军[1,2] 龚柏春[1,2] 朱战霞[1,2] 

机构地区：[1]西北工业大学航天学院,西安710072 [2]航天飞行动力学技术重点实验室,西安710072

出　　处：《中国惯性技术学报》2016年第2期190-195,共6页Journal of Chinese Inertial Technology

基　　金：国家自然科学基金(61004124);中央高校基本科研业务费专项资金资助(3102014KYJD007)

摘　　要：针对空间交会对接地面混合悬浮微重力模拟实验中自主水下航行器的高精度实时导航问题,提出了一种复杂多介质环境下视觉/惯性自适应组合导航方案和算法。首先,以视觉定位系统的采样时标为基准对惯导系统的测量数据进行拟合,并通过惯导逆向导航的方法实现惯导与视觉定位系统的时间同步。然后,根据视觉系统量测噪声时变的特点,设计了可对量测噪声在线估计的指数渐消记忆型Sage-Husa自适应卡尔曼滤波组合导航算法。最后,通过实验验证了该组合方案和算法的有效性。实验结果表明提出的方法可以实现复杂多介质环境下的高精度实时导航,并且相比于测量数据未同步的标准卡尔曼滤波组合导航方式,其速度误差和位置导航误差分别降低了56%和64%。In view that the autonomous underwater vehicle in the grounded microgravity environment of hybrid suspension has accurate real-time navigation problem in spacecraft rendezvous and docking, an adaptive integrated navigation scheme and algorithm was presented based on visual and inertial measurement system under multi-medium environment. The time synchronization between the data of the inertial measurement unit and that of the visual positioning system（VPS） was realized by fitting the inertial measurement data based on the time tag of VPS and backward inertial navigation calculation. Then an adaptive Sage-Husa integrated Kalman filter was designed based on exponent fading factor system with a real-time estimator for the measurement noise to solve the problem of time-varying measurement noise of VPS. The validity of the proposed algorithm was tested and verified by the experiments, and the results show that, compared with the integrated algorithm with common used Kalman filter and without time synchronization, the accurate real-time navigation can be achieved and the velocity/position estimation errors are reduced by 56% and 64% respectively.

关 键 词：视觉定位系统 惯性导航系统 同步 自适应卡尔曼滤波 组合导航 

分 类 号：V249.3[航空宇航科学与技术—飞行器设计]