A FastSLAM algorithm based on the unscented filtering with adaptive selective resampling

Abstract

A FastSLAM approach to the SLAM problem is considered in this paper. An improvement to the classical FastSLAM algorithm has been obtained by replacing the Extended Kalman Filters used in the prediction step and in the feature update with Unscented Kalman Filters and by introducing an adaptive selective resampling. The simulations confirm the effectiveness of the proposed modifications. © 2008 Springer-Verlag Berlin Heidelberg.