A simultaneous localization and mapping (SLAM) based on combined filter is brought forward and use the statistic theory to evaluate the consistency of SLAM algorithm.It decomposes the joint posterior probability distribution into robot path part and feature map part

which make the filter become low dimensional filter and can improve the computational efficiency.The constrained unscented kalman filter (CUKF) make the proposal distribution closer to the posterior probability distribution with new observations and the robot pose can be estimated accurately.The extended kalman filter (EKF) is used to update the feature map location.The simulated experiments show this method is effective and reliable to realize SLAM and its precision can be better than other SLAM algorithms in some environment.