Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

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 condition, including day-night changes, as well as weather and...

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Vydáno v:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition s. 8601 - 8610
Hlavní autoři: Sattler, Torsten, Maddern, Will, Toft, Carl, Torii, Akihiko, Hammarstrand, Lars, Stenborg, Erik, Safari, Daniel, Okutomi, Masatoshi, Pollefeys, Marc, Sivic, Josef, Kahl, Fredrik, Pajdla, Tomas
Médium: Konferenční příspěvek Kapitola
Jazyk:angličtina
Vydáno: IEEE 01.06.2018
Témata:
benchmark
Benchmark testing
camera pose estimation
Cameras
Computer and Information Sciences
Computer graphics and computer vision
Data- och informationsvetenskap (Datateknik)
Datorgrafik och datorseende
long term localization
Natural Sciences
Naturvetenskap
Robots
Solid modeling
Three-dimensional displays
Trajectory
Visual localization
Visualization
ISBN:9781538664209, 1538664208
ISSN:1063-6919, 1063-6919
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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 condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.
ISBN:9781538664209
1538664208
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2018.00897