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Long-Term Visual Localization Revisited

Abstract : Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to linkvirtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, includingday-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camerapose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewingconditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factorson 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches onthese datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, inthe hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas.Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation ofresults on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server
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https://hal.inria.fr/hal-03140805
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Submitted on : Saturday, February 13, 2021 - 10:04:55 PM
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Carl Toft, Will Maddern, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, et al.. Long-Term Visual Localization Revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2020, pp.14. ⟨10.1109/TPAMI.2020.3032010⟩. ⟨hal-03140805⟩

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