Dense RGB-D mapping of large scale environments for real-time localisation and autonomous navigation

Abstract : This paper presents a method and apparatus for building 3D dense visual maps of large scale environments for real-time localisation and autonomous navigation. We propose a spherical ego-centric representation of the environment which is able to reproduce photo-realistic omnidirectional views of captured environments. This representation is composed of a graph of locally accurate augmented spherical panoramas that allows to generate varying viewpoints through novel view synthesis. The spheres are related by a graph of 6 d.o.f. poses which are estimated through multi-view spherical registration. It is shown that this representation can be used to accurately localise a vehicle navigating within the spherical graph, using only a monocular camera for accurate localisation. To perform this task, an efficient direct image registration technique is employed. This approach directly exploits the advantages of the spherical representation by minimising a photometric error between a current image and a reference sphere. Autonomous navigation results are shown in challenging urban environ- ments, containing pedestrians and other vehicles.
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Communication dans un congrès
Intelligent Vehicle (IV'12) Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles, Jun 2012, Alcala de Henares, Spain. 2012
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https://hal.inria.fr/hal-00752897
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  • HAL Id : hal-00752897, version 1

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Maxime Meilland, Patrick Rives, Andrew Comport. Dense RGB-D mapping of large scale environments for real-time localisation and autonomous navigation. Intelligent Vehicle (IV'12) Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles, Jun 2012, Alcala de Henares, Spain. 2012. 〈hal-00752897〉

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