DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions
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.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2022
- DOI:
- 10.48550/arXiv.2202.02556
- arXiv:
- arXiv:2202.02556
- Bibcode:
- 2022arXiv220202556Z
- Keywords:
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- Computer Science - Robotics;
- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- accepted in the 2022 IEEE International Conference on Robotics and Automation (ICRA), Philadelphia (PA), USA