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.
翻译:在本文中,我们展示了一个视觉本地化管道,即MegLoc(MegLoc ), 以便6-DoF(6-DoF)在各种情景下进行稳健和准确的估计,包括室内和室外场景、一天的不同时间、一年、甚至一年的不同季节。 MegLoc(MegLoc)在一系列具有挑战性的数据集上取得了最先进的成果,包括赢得了2021年ICCV(ICCV)在变化条件下长期本地化问题研讨会的门外和室内本地化挑战,以及2021年ICCV(ICCV) 自动驱动关于基于地图的本地化自主驾驶问题研讨会的重新本地化挑战。