The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
This dataset addresses the need for standardized evaluation in visual-inertial odometry for applications like augmented reality and robotics, but it is incremental as it builds on existing benchmarking efforts.
The authors introduced the TUM VI benchmark, a novel dataset for evaluating visual-inertial odometry, providing high-resolution camera images, synchronized IMU data, and accurate ground truth poses, and they tested state-of-the-art methods on it.
Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements tremendously improves tracking accuracy and robustness, and thus has spawned large interest in the development of visual-inertial (VI) odometry approaches. In this paper, we propose the TUM VI benchmark, a novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024x1024 resolution at 20 Hz, high dynamic range and photometric calibration. An IMU measures accelerations and angular velocities on 3 axes at 200 Hz, while the cameras and IMU sensors are time-synchronized in hardware. For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements. The full dataset with raw and calibrated data is publicly available. We also evaluate state-of-the-art VI odometry approaches on our dataset.