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Conference Papers Year : 2007

A framework for scalable vision-only navigation

Abstract

This paper presents a monocular vision framework enabling feature-oriented appearance-based navigation in large outdoor environ- ments containing other moving ob jects. The framework is based on a hybrid topological-geometrical environment representation, constructed from a learning sequence acquired during a robot motion under hu- man control. The framework achieves the desired navigation functional- ity without requiring a global geometrical consistency of the underlying environment representation. The main advantages with respect to con- ventional alternatives are unlimited scalability, real-time mapping and effortless dealing with interconnected environments once the loops have been properly detected. The framework has been validated in demanding, cluttered and interconnected environments, under different imaging con- ditions. The experiments have been performed on many long sequences acquired from moving cars, as well as in real-time large-scale navigation trials relying exclusively on a single perspective camera. The obtained results imply that a globally consistent geometric environment model is not mandatory for successful vision-based outdoor navigation.
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Dates and versions

inria-00350600 , version 1 (07-01-2009)

Identifiers

  • HAL Id : inria-00350600 , version 1

Cite

S. Segvic, A. Remazeilles, A. Diosi, François Chaumette. A framework for scalable vision-only navigation. Advanced Concepts for Intelligent Vision Systems, ACIVS'07, 2007, Delft, Netherlands. pp.112-124. ⟨inria-00350600⟩
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