The SLAM Algorithm for Multiple Robots based on Parameter estimation

With the increasing number of feature points of a map, the dimension of systematic observation is added gradually, which leads to the deviation of the volume points from the desired trajectory and significant errors on the state estimation, An Iterative Squared-Root Cubature Kalman Filter (ISR-CKF) algorithm proposed is aimed at improving the SR-CKF algorithm on the simultaneous localization and mapping (SLAM). By introducing the method of iterative updating, the sample points are re-determined by the estimated value and the square root factor, which keeps the distortion small in the highly nonlinear environment and improves the precision further. A robust tracking Square Root Cubature Kalman Filter algorithm (STF-SRCKF-SLAM) is proposed to solve the problem of reduced accuracy in the condition of state change on the SLAM. The algorithm is predicted according to the kinematic model and observation model of the mobile robot at first, and then the algorithm updates itself by spreading the square root of the error covariance matrix directly, which greatly reduces the computational complexity. At the same time, the time-varying fading factor is introduced in the process of forecasting and updating, and the corresponding weight of the data is adjusted in real time to improve the accuracy of multi-robot localization. The results of simulation shows that the algorithm can improve the accuracy of multi-robot pose effectively.