  Dynamic environments such as urban areas are still challenging for popular
visual-inertial odometry (VIO) algorithms. Existing datasets typically fail to
capture the dynamic nature of these environments, therefore making it difficult
to quantitatively evaluate the robustness of existing VIO methods. To address
this issue, we propose three contributions: firstly, we provide the VIODE
benchmark, a novel dataset recorded from a simulated UAV that navigates in
challenging dynamic environments. The unique feature of the VIODE dataset is
the systematic introduction of moving objects into the scenes. It includes
three environments, each of which is available in four dynamic levels that
progressively add moving objects. The dataset contains synchronized stereo
images and IMU data, as well as ground-truth trajectories and instance
segmentation masks. Secondly, we compare state-of-the-art VIO algorithms on the
VIODE dataset and show that they display substantial performance degradation in
highly dynamic scenes. Thirdly, we propose a simple extension for visual
localization algorithms that relies on semantic information. Our results show
that scene semantics are an effective way to mitigate the adverse effects of
dynamic objects on VIO algorithms. Finally, we make the VIODE dataset publicly
available at https://github.com/kminoda/VIODE.

Robotics