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dc.contributor.authorWang, Chenen_US
dc.contributor.authorJi, Teteen_US
dc.contributor.authorNguyen, Thien-Minhen_US
dc.contributor.authorXie, Lihuaen_US
dc.identifier.citationWang, C., Ji, T., Nguyen, T.-M., & Xie, L. (2018). Correlation flow : robust optical flow using kernel cross-correlators. 2018 IEEE International Conference on Robotics and Automation (ICRA), 836-841. doi:10.1109/ICRA.2018.8460569en_US
dc.description.abstractRobust velocity and position estimation is crucial for autonomous robot navigation. The optical flow based methods for autonomous navigation have been receiving increasing attentions in tandem with the development of micro unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC) based algorithm to determine optical flow using a monocular camera, which is named as correlation flow (CF). Correlation flow is able to provide reliable and accurate velocity estimation and is robust to motion blur. In addition, it can also estimate the altitude velocity and yaw rate, which are not available by traditional methods. Autonomous flight tests on a quadcopter show that correlation flow can provide robust trajectory estimation with very low processing power. The source codes are released based on the ROS framework.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, in any current or future media, including reprinting/republishing this material for adverstising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleCorrelation flow : robust optical flow using kernel cross-correlatorsen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.conference2018 IEEE International Conference on Robotics and Automation (ICRA)en_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsOptical Sensorsen_US
dc.citation.conferencelocationBrisbane, QLD, Australia.en_US
dc.description.acknowledgementThe authors would like to thank Mr. Junjun Wang, Hoang Minh-Chung, and Xu Fang for their help in the experiments. This research was partially supported by the ST Engineering-NTU Corporate Lab funded by the NRF Singapore.en_US
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