StreetMap – Mapping and Localization on Ground Planes using a Downward Facing Camera


This paper describes a system to map a ground-plane, and to subsequently use the map for localization of a mobile robot.

October 1, 2018
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018



Xu Chen (Disney Research)

Anurag Vempati (Disney Research/ETH Joint PhD)

Paul Beardsley (Disney Research)


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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