An improved EKF-SLAM automatic dropping cloth vehicle positioning algorithm

Abstract

In order to improve the positioning accuracy of the automatic dropping cloth vehicle, the problem of increasing error and decreasing positioning accuracy in the strongly nonlinear system is solved by the extended Kalman filter simultaneous localization and mapping (SLAM) algorithm. In this paper, an integrated muti-innovation extended Kalman filter simultaneous localization and mapping (IMI-EKF-SLAM) algorithm was proposed, which enabled the automatic dropping cloth vehicle localization system to change from a single update to multiple iterations in the process of state update, and explored the iteration value n and the new interest dimension p. The simulation and experimental results show that the improved algorithm reduces the state estimation error and the IMI-EKF-SLAM algorithm locates best when the iteration value n is 2 and the new interest dimension p is 3.

Keywords

• extended kalman filter
• simultaneous localization and mapping
• muti-innovation theory
• nonlinear system