Long-Term Visual Localization Revisited

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 4; s. 2074 - 2088
Hlavní autoři: Toft, Carl, Maddern, Will, Torii, Akihiko, Hammarstrand, Lars, Stenborg, Erik, Safari, Daniel, Okutomi, Masatoshi, Pollefeys, Marc, Sivic, Josef, Pajdla, Tomas, Kahl, Fredrik, Sattler, Torsten
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Témata:
6DOF pose estimation
Algorithms
Augmented reality
Autonomous navigation
benchmark
Benchmark testing
Cameras
Computer Science
Computer Vision and Pattern Recognition
Datasets
Localization
long-term localization
Pose estimation
relocalization
Robots
Seasonal variations
Solid modeling
Solid modelling
Test sets
Three-dimensional analysis
Three-dimensional displays
Trajectory
Viewing
Virtual reality
Visual localization
Visualization
Benchmark
Relocalization
Long-term localization
6DOF pose estimation
Visual localization
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the 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 of results 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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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.3032010