Mapping and Spatial Modelling for Navigation (Nato ASI Subseries F:)

Louis F. Pau

ISBN 10: 3540527117 ISBN 13: 9783540527114

Editorial: Springer, 1990

Idioma: Ingl�s

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Detalles bibliogr�ficos

T�tulo

Mapping and Spatial Modelling for Navigation (Nato ASI Subseries F:)

Editor

Springer

Fecha de publicaci�n

1990

Idioma

Ingl�s

ISBN 10

3540527117

ISBN 13

9783540527114

Encuadernaci�n

Hardcover

Estado

Good

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Sinopsis

This volume presents the proceedings of a NATO Advanced Research Workshop (ARW) held in Denmark in August 1989. It results from the activities of the NATO Special Programme on Sensory Systems for Robotic Control, running since 1983 under the auspices of the NATO Science Committee. This volume collects up-to-date interdisciplinary research findings of high practical value, about spatial-data structures, mapping systems, cartographic feature extraction from imagery, mobile robot navigation, and operational and research needs in mapping. As the demand for mapping products and services is growing very rapidly, but the transition to digital map production is still slow, the emphasis in this volume is on specific techniques for map production and their use in various navigation tasks. The key disciplines offering new contributions here are image processing, computational geometry, artificial intelligence, hypermedia, geography, sensor fusion, route planning, and computational environments.

Rese�a del editor

The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form "find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. " Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj.

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