Accurate Camera Registration in Urban Environments Using High-Level Feature Matching

Anil Armagan, Martin Hirzer, Peter M. Roth, Vincent Lepetit

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Abstract

We propose a method for accurate camera pose estimation in urban environments from single images and 2D maps made of the surrounding buildings’ outlines. Our approach bridges the gap between learning-based approaches and geometric approaches: We use recent semantic segmentation techniques for extracting the buildings’ edges and the façades’ normals in the images and minimal solvers [14] to compute the camera pose accurately and robustly. We propose two such minimal solvers: one based on three correspondences of buildings’ corners from the image and the 2D map and another one based on two corner correspondences plus one façade correspondence. We show on a challenging dataset that, compared to recent state-of-the-art [1], this approach is both, faster and more accurate.
Original languageEnglish
Title of host publicationProceedings of the British Machine Vision Conference (BMVC)
Publication statusPublished - 2017
EventBritish Machine Vision Conference 2017 - London, United Kingdom
Duration: 4 Sep 20177 Sep 2017

Conference

ConferenceBritish Machine Vision Conference 2017
Abbreviated titleBMVC 2017
CountryUnited Kingdom
CityLondon
Period4/09/177/09/17

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    Armagan, A., Hirzer, M., Roth, P. M., & Lepetit, V. (2017). Accurate Camera Registration in Urban Environments Using High-Level Feature Matching. In Proceedings of the British Machine Vision Conference (BMVC)