@Article{2018.100000026,
AUTHOR = {MengYuan Chen},
TITLE = {The SLAM Algorithm for Multiple Robots Based on Parameter Estimation},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {3},
PAGES = {593--602},
URL = {http://www.techscience.com/iasc/v24n3/39784},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.31209/2018.100000026}
}