MegLoc: A Robust and Accurate Visual Localization Pipeline
Shuxue Peng, Zihang He, Haotian Zhang, Ran Yan, Chuting Wang, Qingtian, Zhu, Xiao Liu

TL;DR
MegLoc is a new visual localization pipeline that achieves high accuracy and robustness in diverse scenarios, including indoor, outdoor, seasonal, and long-term conditions, outperforming previous methods on multiple challenging datasets.
Contribution
The paper introduces MegLoc, a novel visual localization pipeline that significantly improves robustness and accuracy across various challenging environments and conditions.
Findings
Achieved state-of-the-art results on multiple challenging datasets.
Won the ICCV 2021 challenges for outdoor and indoor localization.
Demonstrated robustness across different seasons and years.
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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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Neural Network Applications · Human Pose and Action Recognition
