StreetMap - Mapping and Localization on Ground Planes using a Downward Facing Camera
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Author / Producer
Date
2018
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
This paper describes a system to map a ground-plane, and to subsequently use the map for localization of a mobile robot. The robot has a downward-facing camera, and works on a variety of ground textures including general texture like tarmac, man-made designs like carpet, and rectilinear textures like indoor tiles or outdoor slabs. Such textures provide a basis for measuring relative motion (i.e. computer mouse functionality). But the goal here is the more challenging one of absolute localization. The paper describes a complete working pipeline to build a globally consistent map of a given ground-plane and subsequently to localize within this map at real-time. Two algorithms are described. The first is a feature-based approach which is general to any ground plane texture. The second algorithm takes advantage of the extra constraints available for common rectilinear textures like indoor tiling, paving slabs, and laid brickwork. Quantitative and qualitative experimental results are shown for mapping and localization on a variety of ground-planes.
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Publication status
published
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Editor
Book title
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
1672 - 1679
Publisher
IEEE
Event
25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)