TY - EJOU
AU - Chen, MengYuan
TI - The SLAM Algorithm for Multiple Robots Based on Parameter Estimation
T2 - Intelligent Automation \& Soft Computing
PY - 2018
VL - 24
IS - 3
SN - 2326-005X
AB - 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.
KW - Iteration
KW - Simultaneous localization and mapping
KW - Sampling
KW - Multi-mobile robot
KW - Square Root Cubature Kalman Filter
KW - Strong tracking
DO - 10.31209/2018.100000026