Improvement of the simultaneous localization and map building algorithm applying scaled unscented transformation

This article applies scaled unscented transformation to the simultaneous localization and map building algorithm in two different ways. One is for the entire vehicle-map states by replacing EKF with the unscented Kalman filtering (UKF) to carry on the state estimation; the other is for the vehicle states by using the EKF both in the prediction of the map feature and the update of the complete state vector. The plentiful Monter-Carlo simulations were carried out to evaluate the algorithms´ performance. The simulation results indicate that both two methods can reduce the EKF linearization error effectively, and the second method is more efficient in computation.