  The lack of realistic and open benchmarking datasets for pedestrian
visual-inertial odometry has made it hard to pinpoint differences in published
methods. Existing datasets either lack a full six degree-of-freedom
ground-truth or are limited to small spaces with optical tracking systems. We
take advantage of advances in pure inertial navigation, and develop a set of
versatile and challenging real-world computer vision benchmark sets for
visual-inertial odometry. For this purpose, we have built a test rig equipped
with an iPhone, a Google Pixel Android phone, and a Google Tango device. We
provide a wide range of raw sensor data that is accessible on almost any
modern-day smartphone together with a high-quality ground-truth track. We also
compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple
ARKit with two recent methods published in academic forums. The data sets cover
both indoor and outdoor cases, with stairs, escalators, elevators, office
environments, a shopping mall, and metro station.
