Abstract
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 diffi-
culty 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