Abstract:

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 iterated EKF was proposed, which were also called IFastSLAM algorithm. The iterated EKF were used to estimate the particle 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 consistency of the map better.

Key words: FastSLAM2.0 algorithm, the iterated EKF filter, IFastSLAM algorithm