Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

@article{Sattler2018Benchmarking6O,
  title={Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions},
  author={Torsten Sattler and William P. Maddern and Carl Toft and Akihiko Torii and Lars Hammarstrand and Erik Stenborg and Daniel Safari and M. Okutomi and Marc Pollefeys and Josef Sivic and Fredrik Kahl and Tom{\'a}s Pajdla},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={8601-8610}
}
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… 
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353 Citations
Highly Influential Citations
57
Background Citations
194
Methods Citations
147
Results Citations
8

353 Citations

Long-Term Visual Localization Revisited
TLDR
Three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, are extended, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible and the performance of state-of-the-art localization approaches on these datasets discussed.
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
TLDR
A new large-scale visual localization method targeted for indoor spaces that significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data.
VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments
TLDR
VIZARD, a visual localization system for urban outdoor environments, is presented, which is able to achieve nearly 100% recall with a localization accuracy below 0.5m under varying outdoor appearance conditions, including at night-time.
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
TLDR
A new large-scale visual localization method targeted for indoor environments that significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data.
Improving Image Description with Auxiliary Modality for Visual Localization in Challenging Conditions
TLDR
This work proposes a new approach for outdoor large scale image-based localization that can deal with challenging scenarios like cross-season, cross-weather and day/night localization, and shows through extensive evaluation that this method can improve localization performances, especially in challenging scenarios when the visual appearance of the scene has changed.
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
TLDR
This paper adapts 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re- localization benchmark focused on indoor scenes and explores how state-of-the-art cameraRe-localizers perform according to these metrics.
Learning Scene Geometry for Visual Localization in Challenging Conditions
TLDR
A new approach for outdoor large scale image based localization that can deal with challenging scenarios like cross-season, cross-weather, day/night and long-term localization and a new learned global image descriptor that can effectively benefit from scene geometry information during training is proposed.
Large-scale Localization Datasets in Crowded Indoor Spaces
TLDR
A robust LiDAR SLAM is developed which provides initial poses that are then refined using a novel structure-from-motion based optimization and presented a benchmark of modern visual localization algorithms on these challenging datasets showing superior performance of structure-based methods using robust image features.
Efficient Visual Localization for Ground Vehicles in Outdoor Environments
TLDR
It is demonstrated that reliable and accurate visual localization is feasible in structured outdoor environments, even over long time spans, across vastly different seasonal seasons.
ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay
TLDR
A novel method for automatically discovering which points in a map remain stable over time, and which are due to transient changes is proposed, weighted by an exponential decay over time.
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