基于组合EKF的自主水下航行器SLAM  

SLAM of AUV Based on the Combined EKF

作　　者：王宏健[1] 王晶[1] 边信黔[1] 傅桂霞 

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001

出　　处：《机器人》2012年第1期56-64,共9页Robot

基　　金：国家自然科学基金资助项目(E091002/50979017);教育部高等学校博士学科点专项科研基金资助项目(20092304110008);中央高校基本科研业务费专项资金资助项目(HEUCFU 1026)

摘　　要：针对标准扩展卡尔曼滤波(EKF)在噪声统计特性不准确、系统模型与实际模型无法完全匹配情况下滤波精度严重下降的问题,提出了一种基于Sage-Husa自适应EKF和强跟踪EKF组合的SLAM(同步定位与地图构建)算法.首先建立了AUV(自主水下航行器)的动力学模型、特征模型以及传感器的测量模型,然后通过Hough变换进行特征提取,最终采用组合EKF实现了自主水下航行器的同步定位与地图构建.海试数据仿真试验表明本文所提方法降低了噪声统计特性时变以及模型不精确对系统的影响,提高了SLAM系统的精确性和鲁棒性.A simultaneous localization and mapping（SLAM） algorithm based on the combined EKF（extended Kalman filter） of Sage-Husa adaptive EKF and strong tracking EKF is presented to solve the decrease of filtering accuracy of standard EKF when the statistical characteristics of noise are not accurate and the model builded can not match with the actual one completely.Firstly,the dynamic model,feature model and sensor measurement model of AUV（autonomous underwater vehicle） are set up.Then,feature extraction is implemented through Hough transform,and SLAM of AUV is realized with the combined EKF eventually.Simulation with trial data shows that the described method reduces the influence of both the time-variance of statistical characteristics of noise and the inaccuracy of model,and enhances the accuracy and robustness of SLAM system.

关 键 词：同步定位与地图构建 EKF Sage-Husa自适应EKF 强跟踪EKF 组合EKF 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]