DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions

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Abstract. 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.

Reference:

• Zuo, Y.F., Yang, J., Chen, J., Wang, X., Wang, Y. and Kneip, L., 2022. DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions. In 2022 IEEE International Conference on Robotics and Automation (ICRA). IEEE.