“Sensor Integration Using State Estimators”
Authors: Jens G. Balchen, Fredrik Dessen and G. Skofteland,Affiliation: NTNU, Department of Engineering Cybernetics
Reference: 1991, Vol 12, No 2, pp. 69-80.
Keywords: Sensor integration, Kalman filter, 3-D motion estimation, Robot vision
Abstract: Means for including very different types of sensors using one single unit are described. Accumulated data are represented using an updatable dynamic model, a Kalman filter. The scheme handles common phenomena such as skewed sampling, finite resolution measurements and information delays. Included is an example where 3D motion information is collected by one or more vision sensors.
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DOI: 10.4173/mic.1991.2.2References:
[1] BROIDA, T.J. CHELLAPPA, R. (1986). Estimation of Object Motion Parameters from Noisy Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, 8, 90-98 doi:10.1109/TPAMI.1986.4767755
[2] DICKMANNS, E.D. ZAPP, A. (1985). Guiding Land Vehicles along Roadways by Computer Vision, Proc. Congrés Automatique 1985, The Tools for Tomorrow, Toulouse, 233-244.
[3] JAZWINSKI, A.H. (1970). Stochastic Processes and Filtering Theory, Academic Press, New York.
BibTeX:
@article{MIC-1991-2-2,
title={{Sensor Integration Using State Estimators}},
author={Balchen, Jens G. and Dessen, Fredrik and Skofteland, G.},
journal={Modeling, Identification and Control},
volume={12},
number={2},
pages={69--80},
year={1991},
doi={10.4173/mic.1991.2.2},
publisher={Norwegian Society of Automatic Control}
};