  This paper presents benchmark tests of various visual(-inertial) odometry
algorithms on NVIDIA Jetson platforms. The compared algorithms include mono and
stereo, covering Visual Odometry (VO) and Visual-Inertial Odometry (VIO):
VINS-Mono, VINS-Fusion, Kimera, ALVIO, Stereo-MSCKF, ORB-SLAM2 stereo, and
ROVIO. As these methods are mainly used for unmanned aerial vehicles (UAVs),
they must perform well in situations where the size of the processing board and
weight is limited. Jetson boards released by NVIDIA satisfy these constraints
as they have a sufficiently powerful central processing unit (CPU) and graphics
processing unit (GPU) for image processing. However, in existing studies, the
performance of Jetson boards as a processing platform for executing VO/VIO has
not been compared extensively in terms of the usage of computing resources and
accuracy. Therefore, this study compares representative VO/VIO algorithms on
several NVIDIA Jetson platforms, namely NVIDIA Jetson TX2, Xavier NX, and AGX
Xavier, and introduces a novel dataset 'KAIST VIO dataset' for UAVs. Including
pure rotations, the dataset has several geometric trajectories that are harsh
to visual(-inertial) state estimation. The evaluation is performed in terms of
the accuracy of estimated odometry, CPU usage, and memory usage on various
Jetson boards, algorithms, and trajectories. We present the {results of the}
comprehensive benchmark test and release the dataset for the computer vision
and robotics applications.
