MegLoc: A Robust and Accurate Visual Localization Pipeline
It addresses the problem of accurate visual localization for applications like autonomous driving and robotics under changing environmental conditions.
The paper tackles robust 6-DoF visual localization across varying conditions like indoor/outdoor scenes, time, seasons, and years, achieving state-of-the-art results by winning multiple ICCV 2021 workshop challenges.
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.