Pseudo-measured LPV Kalman filter for SLAM
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hdl:2117/17524
Document typeConference report
Defense date2012
PublisherInstitute of Electrical and Electronics Engineers
Rights accessRestricted access - publisher's policy
Abstract
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.
CitationGuerra, 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.
ISBN9781467303101
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