  Event cameras are bio-inspired vision sensors which measure per pixel
brightness changes. They offer numerous benefits over traditional, frame-based
cameras, including low latency, high dynamic range, high temporal resolution
and low power consumption. Thus, these sensors are suited for robotics and
virtual reality applications. To foster the development of 3D perception and
navigation algorithms with event cameras, we present the TUM-VIE dataset. It
consists of a large variety of handheld and head-mounted sequences in indoor
and outdoor environments, including rapid motion during sports and high dynamic
range scenarios. The dataset contains stereo event data, stereo grayscale
frames at 20Hz as well as IMU data at 200Hz. Timestamps between all sensors are
synchronized in hardware. The event cameras contain a large sensor of 1280x720
pixels, which is significantly larger than the sensors used in existing stereo
event datasets (at least by a factor of ten). We provide ground truth poses
from a motion capture system at 120Hz during the beginning and end of each
sequence, which can be used for trajectory evaluation. TUM-VIE includes
challenging sequences where state-of-the art visual SLAM algorithms either fail
or result in large drift. Hence, our dataset can help to push the boundary of
future research on event-based visual-inertial perception algorithms.
