%0 Journal Article
%T Novel FastSLAM algorithm based on square root unscented Kalman filter<br>一种基于SR-UKF的FastSLAM算法
%A LV Tai-zhi
%A ZHAO Chun-xia
%A <br>吕太之
%A 赵春霞
%J 计算机应用研究
%D 2012
%I 
%X Standard FastSLAM algorithm suffers from particle set degeneracy and accumulation errors caused by linearization of the nonlinear model. To overcome the above problems, this paper proposed a novel FastSlam algorithm based on square root unscented Kalman filterSR-UKF. SR-UKF selected a group of representative sigma points to approximate the covariance, these sigma points were propageted through the non-linearforce model to reconstruct the new statistical characteristics. Using SR-UKF to replace EKF for posteriori estimation of particles could reduce the linearization error and slow down particle set degeneracy. SR-UKF ensured the non-negative definite of covariance matrix to guarantee the stability of SLAM algorithm. The simulation experiments demonstrate that the proposed algorithm is better than FastSLAM 2. 0 both in accuracy and robustness.
%K simultaneous localization and mapping（SLAM）
%K square root unscented Kalman filter（SR-UKF）
%K fast simulta-neous location and mapping（FastSLAM）
%K extended Kalman filter（EKF）<br>同时定位与地图创建
%K 基于平方根的无迹卡尔曼滤波
%K 快速同时定位与地图创建
%K 扩展卡尔曼滤波
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=38AAB79AD8A56B53B4E90CB727D700AB&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=F3090AE9B60B7ED1&sid=DED51F1DB9E2EBA9&eid=4231CFBD7C99A971&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11