Pseudo-measured LPV Kalman Filter for SLAM
This paper describes a new approach to the wellknown robotics problem of simultaneous location and mapping (SLAM). The proposed technique introduces a linear varying parameter (LPV) modeling solution for the estimation of nonlinear models in a Kalman Filter based algorithm. In this technique, the estimation model for the robotic device considered is modeled as a quasi-LPV model, which in turn, is linearized around a set of given points of the varying parameter. The observation model is rearranged into a pseudo-measurement model, which is used in form of a pseudo-linear model during the update stage of the Kalman filter. The initial tests and experimentations suggest that this technique can improve Extended Kalman Filter SLAM results by avoiding a great deal of the bias introduced by linearization of nonlinear models into EKF equations.
Kalman Filter pseudolinear modelling linear varying parameter LPV SLAM
Edmundo Guerra Yolanda Bolea Antoni Grau
Automatic Control Dept, Technical University of Catalonia, UPCBarcelona, Spain Automatic Control Dept, Technical University of Catalonia, UPC Barcelona, Spain
国际会议
IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)
北京
英文
700-705
2012-07-25（万方平台首次上网日期，不代表论文的发表时间）