  Estimating the precise location of a camera using visual localization enables
interesting applications such as augmented reality or robot navigation. This is
particularly useful in indoor environments where other localization
technologies, such as GNSS, fail. Indoor spaces impose interesting challenges
on visual localization algorithms: occlusions due to people, textureless
surfaces, large viewpoint changes, low light, repetitive textures, etc.
Existing indoor datasets are either comparably small or do only cover a subset
of the mentioned challenges. In this paper, we introduce 5 new indoor datasets
for visual localization in challenging real-world environments. They were
captured in a large shopping mall and a large metro station in Seoul, South
Korea, using a dedicated mapping platform consisting of 10 cameras and 2 laser
scanners. In order to obtain accurate ground truth camera poses, we developed a
robust LiDAR SLAM which provides initial poses that are then refined using a
novel structure-from-motion based optimization. We present a benchmark of
modern visual localization algorithms on these challenging datasets showing
superior performance of structure-based methods using robust image features.
The datasets are available at: https://naverlabs.com/datasets
