  Event cameras offer the exciting possibility of tracking the camera's pose
during high-speed motion and in adverse lighting conditions. Despite this
promise, existing event-based monocular visual odometry (VO) approaches
demonstrate limited performance on recent benchmarks. To address this
limitation, some methods resort to additional sensors such as IMUs, stereo
event cameras, or frame-based cameras. Nonetheless, these additional sensors
limit the application of event cameras in real-world devices since they
increase cost and complicate system requirements. Moreover, relying on a
frame-based camera makes the system susceptible to motion blur and HDR. To
remove the dependency on additional sensors and to push the limits of using
only a single event camera, we present Deep Event VO (DEVO), the first
monocular event-only system with strong performance on a large number of
real-world benchmarks. DEVO sparsely tracks selected event patches over time. A
key component of DEVO is a novel deep patch selection mechanism tailored to
event data. We significantly decrease the pose tracking error on seven
real-world benchmarks by up to 97% compared to event-only methods and often
surpass or are close to stereo or inertial methods. Code is available at
https://github.com/tum-vision/DEVO
