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Improving Robustness of Monocular Urban Localization using Augmented Street View

Abstract : — With the fast development of Geographic Information Systems, visual global localization has gained a lot of attention due to the low price of a camera and the practical implications. In this paper, we leverage Google Street View and a monocular camera to develop a refined and continuous positioning in urban environments: namely a topological visual place recognition and then a 6 DoF pose estimation by local bundle adjustment. In order to avoid discrete localization problems, augmented Street View data are virtually synthesized to render a smooth and metric localization. We also demonstrate that this approach significantly improves the sub-meter accuracy and the robustness to important viewpoint changes, illumination and occlusion.
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https://hal.archives-ouvertes.fr/hal-01425632
Contributor : Fabien Moutarde <>
Submitted on : Tuesday, January 3, 2017 - 4:54:21 PM
Last modification on : Thursday, April 9, 2020 - 5:08:13 PM
Document(s) archivé(s) le : Tuesday, April 4, 2017 - 2:45:47 PM

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  • HAL Id : hal-01425632, version 1

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Li Yu, Cyril Joly, Guillaume Bresson, Fabien Moutarde. Improving Robustness of Monocular Urban Localization using Augmented Street View. 19th IEEE International Conference on Intelligent Transportation Systems (ITSC'2016), Nov 2016, Rio de Janeiro, Brazil. ⟨hal-01425632⟩

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