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arXiv · 2111.13063

MegLoc: A Robust and Accurate Visual Localization Pipeline

Shuxue Peng·Zihang He·Haotian Zhang·Ran Yan·Chuting Wang·Qingtian Zhu·Xiao Liu
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Abstract

In this paper, we present a visual localization pipeline, namely MegLoc, for robust and accurate 6-DoF pose estimation under varying scenarios, including indoor and outdoor scenes, different time across a day, different seasons across a year, and even across years. MegLoc achieves state-of-the-art results on a range of challenging datasets, including winning the Outdoor and Indoor Visual Localization Challenge of ICCV 2021 Workshop on Long-term Visual Localization under Changing Conditions, as well as the Re-localization Challenge for Autonomous Driving of ICCV 2021 Workshop on Map-based Localization for Autonomous Driving.

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Shuxue Peng, Zihang He, Haotian Zhang, Ran Yan, Chuting Wang, Qingtian Zhu, Xiao Liu. 2021-11-25. MegLoc: A Robust and Accurate Visual Localization Pipeline. https://arxiv.org/abs/2111.13063

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