Adaptive square-root CKF based SLAM algorithm for indoor UGVs

This paper introduced a simultaneous localization and mapping algorithm for unmanned ground vehicles (UGVs), which using square-root version of Cubature Kalman Filter to reduce the computation complexity and to improve the numerical stability, meanwhile adaptive factors was used to make the estimated square-root of the covariance more accurate. The range and angle measurements from light detection and ranging (LiDAR) are fused with odometer and a reduced set of inertial sensors using proposed algorithm. The performance was verified through simulation and real experiment, which showed excellent estimation and reliable accuracy.