NDLI: Outdoor robot navigation based on a probabilistic data fusion scheme

Content Provider	IEEE Xplore Digital Library
Author	Mirats Tur, J.M. Borja, C.A.
Copyright Year	2007
Description	Author affiliation: Inst. de Robotica i Inf. Ind., Barcelona (Mirats Tur, J.M.; Borja, C.A.)
Abstract	This article presents a data fusion method which seeks to obtain better pose estimation of a mobile robot through obtaining a more accurate covariance uncertainty matrix. We seek to compute the state covariance without using the first-order linear approximations of the extended Kalman filter. We consider, unlike standard work done in error propagation and data fusion, the possible correlation between the different sensor pose estimates, odometry and DGPS for the present work, and the autocorrelation of some of the variables involved in the fusion (DGPS data, for the particular case herein presented). Computation of the covariances of each sensor data vector is presented so it takes into account the vehicle kinematics, and hence, its particular characteristics. In order to validate the presented approach, a real outdoor navigation experiment is presented fusing odometry and DGPS data.
Starting Page	3733
Ending Page	3738
File Size	307189
Page Count	6
File Format	PDF
ISBN	9781424409112
DOI	10.1109/IROS.2007.4399108
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Navigation Global Positioning System Mobile robots Uncertainty Covariance matrix Linear approximation Sensor fusion Autocorrelation Sensor phenomena and characterization Vehicles robot navigation Data fusion uncertainty position estimation robot localization
Content Type	Text
Resource Type	Article

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