Novel SLAM algorithm for UGVs based on unscented Kalman filtering

In this paper, a new SLAM solution for unmanned ground vehicle (UGV), based on the combination of extended Kalman filter (EKF) and unscented Kalman filter (UKF), is proposed. The EKF is used to calculate incremental motion parameters of UGV according to the raw data acquired from both IMU and vehicle sensors, while the UKF to simultaneously estimate the position and orientation of UGV and the locations of landmarks. In order to reduce the computational complexity, we present a new landmark sampling strategy for the UKF-based SLAM. It leads to a constant computational cost, independent of the number of landmarks observed. The experimental results obtained from real-world road tests validate the performance of our SLAM solution.