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Title: Correlation flow : robust optical flow using kernel cross-correlators
Authors: Wang, Chen
Ji, Tete
Nguyen, Thien-Minh
Xie, Lihua
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Wang, 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.2018.8460569
Conference: 2018 IEEE International Conference on Robotics and Automation (ICRA)
Abstract: Robust 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.
ISBN: 978-1-5386-3081-5
DOI: 10.1109/ICRA.2018.8460569
Schools: School of Electrical and Electronic Engineering 
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:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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