  We present a novel real-time visual odometry framework for a stereo setup of
a depth and high-resolution event camera. Our framework balances accuracy and
robustness against computational efficiency towards strong performance in
challenging scenarios. We extend conventional edge-based semi-dense visual
odometry towards time-surface maps obtained from event streams. Semi-dense
depth maps are generated by warping the corresponding depth values of the
extrinsically calibrated depth camera. The tracking module updates the camera
pose through efficient, geometric semi-dense 3D-2D edge alignment. Our approach
is validated on both public and self-collected datasets captured under various
conditions. We show that the proposed method performs comparable to
state-of-the-art RGB-D camera-based alternatives in regular conditions, and
eventually outperforms in challenging conditions such as high dynamics or low
illumination.
