Square-root unscented Kalman filter based simultaneous localization and mapping

Abstract

Simultaneous localization and mapping (SLAM) is concerned to be the key point to realize the real autonomy of mobile robot. Unscented Kalman filter (UKF) is widely applied in SLAM problem because of its directly using of nonlinear model. Concerning that square root filter can ensure non-negative definite of the covariance matrix, this article introduced a square-root unscented Kalman filter into SLAM problem and ensured its stability. This algorithm also gained a more accurate estimation compared to UKF based SLAM. Simulation results showed that this algorithm is effective.