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ShanghaiTech University Knowledge Management System
DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions | |
2022 | |
会议录名称 | PROCEEDINGS - IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION |
ISSN | 1050-4729 |
页码 | 2179-2185 |
发表状态 | 已发表 |
DOI | 10.1109/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 |
URL | 查看原文 |
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 |
引用统计 | 正在获取...
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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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