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DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions
2022
会议录名称PROCEEDINGS - IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
ISSN1050-4729
页码2179-2185
发表状态已发表
DOI10.1109/ICRA46639.2022.9811805
摘要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. © 2022 IEEE.
会议录编者/会议主办者IEEE ; IEEE Robotics and Automation Society (RA)
关键词Computational efficiency Computer vision Stereo image processing Vision Condition Dense depth map Depth resolution Edge-based Event streams High resolution Performance Real- time Surface map Visual odometry
会议名称39th IEEE International Conference on Robotics and Automation, ICRA 2022
会议地点Philadelphia, PA, United states
会议日期May 23, 2022 - May 27, 2022
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收录类别EI
语种英语
出版者Institute of Electrical and Electronics Engineers Inc.
EI入藏号20223312572945
EI主题词Cameras
EI分类号723.2 Data Processing and Image Processing ; 723.5 Computer Applications ; 741.2 Vision ; 742.2 Photographic Equipment
原始文献类型Conference article (CA)
来源库IEEE
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文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/223058
专题信息科学与技术学院_PI研究组_Laurent Kneip组
信息科学与技术学院_硕士生
信息科学与技术学院_本科生
信息科学与技术学院_博士生
作者单位
1.Key Laboratory of Optoelectronic Imaging Technology and Systems, Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
2.Mobile Perception Lab, ShanghaiTech University, Shanghai Engineering Research Center of Intelligent Vision and Imaging
推荐引用方式
GB/T 7714
Yi–Fan Zuo,Jiaqi Yang,Jiaben Chen,et al. DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions[C]//IEEE, IEEE Robotics and Automation Society (RA):Institute of Electrical and Electronics Engineers Inc.,2022:2179-2185.
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