  In this paper, we present a dataset capturing diverse visual data formats
that target varying luminance conditions. While RGB cameras provide nourishing
and intuitive information, changes in lighting conditions potentially result in
catastrophic failure for robotic applications based on vision sensors.
Approaches overcoming illumination problems have included developing more
robust algorithms or other types of visual sensors, such as thermal and event
cameras. Despite the alternative sensors' potential, there still are few
datasets with alternative vision sensors. Thus, we provided a dataset recorded
from alternative vision sensors, by handheld or mounted on a car, repeatedly in
the same space but in different conditions. We aim to acquire visible
information from co-aligned alternative vision sensors. Our sensor system
collects data more independently from visible light intensity by measuring the
amount of infrared dissipation, depth by structured reflection, and
instantaneous temporal changes in luminance. We provide these measurements
along with inertial sensors and ground-truth for developing robust visual SLAM
under poor illumination. The full dataset is available at:
https://visibilitydataset.github.io/
