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.

通过使用摄像头和惯性测量单元（IMU）传感器，Visual Inertial Odometry（VIO）算法可以准确估计相机轨迹。我们引入了 Amirkabir 校园数据集（AUT-VI）来解决动态环境下的问题，改善导航系统。AUT-VI 是一个新颖而超级具有挑战性的数据集，包含 17 个不同位置的 126 个多样的序列，涵盖了所有极端导航场景。我们还发布了安卓应用程序用于数据捕获，以支持研究人员定制自己的 VIO 数据集变体，并在我们的数据集上评估了最先进的 Visual Inertial Odometry（VIO）和 Visual Odometry（VO）方法，强调了这个具有挑战性数据集的重要性。