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Principles and Techniques for Sensor Data Fusion

  • Conference paper
Multisensor Fusion for Computer Vision

Part of the book series: NATO ASI Series ((NATO ASI F,volume 99))

Abstract

This paper concerns a problem which is basic to perception: The integration of perceptual information into a coherent description of the world. In this paper we present perception as a process of dynamically maintaining a model of the local external environment. Perceptual fusion is at the heart of this process.

After a brief introduction, we review the background of the problem of fusion in machine vision. We then present fusion as part of the process of dynamic world modeling, and postulate a set of principles for the “fusion” of independent observations. These principles lead to techniques which permit perceptual fusion with qualitatively different forms of data, treating each source of information as a constraint.. For numerical information, these principles lead to specific well known tools such as various forms of Kalman filter and Mahalanobis distance. For symbolic information, these principles suggest representing categories of objects as a conjunction of properties.

Dynamic world modeling is a cyclic process composed of the phases: predict, match and update. We show that in the case of numerical observations, these principals leads to the use Kalman filter techniques for the prediction and update phases, while a Mahalanobis distance is used for matching. These techniques are illustrated with examples from existing systems. We then speculate on the extension of these techniques to symbolic information.

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Author information

Authors and Affiliations

  1. LIFIA (IMAG), Grenoble, France

    James L. Crowley

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  1. James L. Crowley
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Editor information

Editors and Affiliations

  1. Computer and Vision Research Center Department of Electrical and Computer Engineering, The University of Texas at Austin, 78712-1084, Austin, TX, USA

    J. K. Aggarwal (Director) (Director)

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© 1993 Springer-Verlag Berlin Heidelberg

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Crowley, J.L. (1993). Principles and Techniques for Sensor Data Fusion. In: Aggarwal, J.K. (eds) Multisensor Fusion for Computer Vision. NATO ASI Series, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02957-2_2

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  • DOI: https://doi.org/10.1007/978-3-662-02957-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08135-4

  • Online ISBN: 978-3-662-02957-2

  • eBook Packages: Springer Book Archive

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