MengYuan Chen1,2
Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 593-602, 2018, DOI:10.31209/2018.100000026
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… More >
Login
Register
