机器人2012，Vol.34Issue(1)：56-64,9.DOI:10.3724/SP.J.1218.2012.00056
基于组合EKF的自主水下航行器SLAM
SLAM of AUV Based on the Combined EKF
摘要
Abstract
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EKFKey words
SLAM (simultaneous localization and mapping)/ EKF (extended Kalman filter)/ Sage-Husa adaptive EKF/ strong tracking EKF/ combined EKF分类
信息技术与安全科学引用本文复制引用
王宏健,王晶,边信黔,傅桂霞..基于组合EKF的自主水下航行器SLAM[J].机器人,2012,34(1):56-64,9.基金项目
国家自然科学基金资助项目(E091002/50979017) （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) （20092304110008）
中央高校基本科研业务费专项资金资助项目(HEUCFU 1026). （HEUCFU 1026）
