Pseudo-measured LPV Kalman filter for SLAM
Tipo de documentoTexto en actas de congreso
Fecha de publicación2012
EditorInstitute of Electrical and Electronics Engineers
Condiciones de accesoAcceso restringido por política de la editorial
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.
CitaciónGuerra, E.; Bolea, Y.; Grau, A. Pseudo-measured LPV Kalman filter for SLAM. A: IEEE International Conference on Industrial Informatics. "INDIN 2012: IEEE 10th International Conference on Industrial Informatics: 25-27 July, 2012, Beijing, China". Beijing: Institute of Electrical and Electronics Engineers, 2012, p. 700-705.
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