  Commercial visual-inertial odometry (VIO) systems have been gaining attention
as cost-effective, off-the-shelf six degrees of freedom (6-DoF) ego-motion
tracking methods for estimating accurate and consistent camera pose data, in
addition to their ability to operate without external localization from motion
capture or global positioning systems. It is unclear from existing results,
however, which commercial VIO platforms are the most stable, consistent, and
accurate in terms of state estimation for indoor and outdoor robotic
applications. We assess four popular proprietary VIO systems (Apple ARKit,
Google ARCore, Intel RealSense T265, and Stereolabs ZED 2) through a series of
both indoor and outdoor experiments where we show their positioning stability,
consistency, and accuracy. We present our complete results as a benchmark
comparison for the research community.
