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This paper presents benchmark tests of various visual(-inertial) odometryalgorithms on NVIDIA Jetson platforms. The compared algorithms include mono andstereo, covering Visual Odometry (VO) and Visual-Inertial Odometry (VIO):VINS-Mono, VINS-Fusion, Kimera, ALVIO, Stereo-MSCKF, ORB-SLAM2 stereo, andROVIO. As these methods are mainly used for unmanned aerial vehicles (UAVs),they must perform well in situations where the size of the processing board andweight is limited. Jetson boards released by NVIDIA satisfy these constraintsas they have a sufficiently powerful central processing unit (CPU) and graphicsprocessing unit (GPU) for image processing. However, in existing studies, theperformance of Jetson boards as a processing platform for executing VO/VIO hasnot been compared extensively in terms of the usage of computing resources andaccuracy. Therefore, this study compares representative VO/VIO algorithms onseveral NVIDIA Jetson platforms, namely NVIDIA Jetson TX2, Xavier NX, and AGXXavier, and introduces a novel dataset 'KAIST VIO dataset' for UAVs. Includingpure rotations, the dataset has several geometric trajectories that are harshto visual(-inertial) state estimation. The evaluation is performed in terms ofthe accuracy of estimated odometry, CPU usage, and memory usage on variousJetson boards, algorithms, and trajectories. We present the {results of the}comprehensive benchmark test and release the dataset for the computer visionand robotics applications.

Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle