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Dense visual mapping of large scale environments for real-time localisation

Abstract : This paper presents a method and apparatus for building dense visual maps of large scale 3D environments for real-time localisation and navigation. A spherical ego-centric representation of the environment is proposed that is able to reproduce photo-realistic omnidirectional views of captured environments. This representation is novel in that it 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 6dof poses which are estimated through multi-view spherical registration. To acquire these models, a multi-baseline acquisition system has been designed and built which is based on an outward facing ring of cameras with diverging views. This configuration allows to capture high resolution spherical images of the environment and compute a dense depth map through a wide baseline dense correspondence algorithm. A calibration procedure is developed for an outward facing camera ring that imposes a loop closing constraint, in order to obtain a consistent set of extrinsic parameters. This spherical sensor is shown to acquire compact, accurate and efficient representations of large environments and is used for real-time model-based localisation.
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Submitted on : Monday, August 19, 2019 - 11:49:05 AM
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  • HAL Id : hal-01357369, version 1



Maxime Meilland, Andrew I. Comport, Patrick Rives. Dense visual mapping of large scale environments for real-time localisation. IEEE/RSJ International Conference on Intelligent Robots and System, 2011, San Francisco, California, United States. ⟨hal-01357369⟩



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