  Visual Inertial Odometry (VIO) algorithms estimate the accurate camera
trajectory by using camera and Inertial Measurement Unit (IMU) sensors. The
applications of VIO span a diverse range, including augmented reality and
indoor navigation. VIO algorithms hold the potential to facilitate navigation
for visually impaired individuals in both indoor and outdoor settings.
Nevertheless, state-of-the-art VIO algorithms encounter substantial challenges
in dynamic environments, particularly in densely populated corridors. Existing
VIO datasets, e.g., ADVIO, typically fail to effectively exploit these
challenges. In this paper, we introduce the Amirkabir campus dataset (AUT-VI)
to address the mentioned problem and improve the navigation systems. AUT-VI is
a novel and super-challenging dataset with 126 diverse sequences in 17
different locations. This dataset contains dynamic objects, challenging
loop-closure/map-reuse, different lighting conditions, reflections, and sudden
camera movements to cover all extreme navigation scenarios. Moreover, in
support of ongoing development efforts, we have released the Android
application for data capture to the public. This allows fellow researchers to
easily capture their customized VIO dataset variations. In addition, we
evaluate state-of-the-art Visual Inertial Odometry (VIO) and Visual Odometry
(VO) methods on our dataset, emphasizing the essential need for this
challenging dataset.
