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中文摘要：
针对在传统的快速地图创建和同时定位算法（fast simultaneous location and map building，Fast SLAM）中采用扩展卡尔曼滤波器（extend Kalman filter，EKF）来估计机器人位姿和地图创建所带来的线性化误差的问题，本研究提出了一种基于迭代EKF的Fast SLAM2．0算法——IFastsLAM算法。该算法将迭代思想运用到EKF中，同时采用迭代EKF来估计粒子从而完成机器人地图创建和自身定位。实验结果证明，该算法提高了粒子的估计精度从而减缓粒子退化问题，并更好的维持了地图的一致性。
英文摘要：
The traditional fast map building and positioning algorithm for fast simultaneous location and map building （FastSLAM） usually used the extend Kalman filter （EKF）to estimate the robot＇ s pose and map, which could lead to some problems of linearization error. In order to solve this problem, a new FastSLAM2. 0 algorithm based on the itera- ted EKF was proposed, which were also called IFastSLAM algorithm. The iterated EKF were used to estimate the parti- cle and then to complete the map building and self-positioning. The experimental results showed that this algorithm could improve the accuracy of estimating particle to slow down the particle degradation, and could maintain the consis- tency of the map better.
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