@inproceedings{75aaac94f724415e93d33c771c21f70d,
title = "Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators",
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.",
author = "Chen Wang and Tete Ji and Nguyen, \{Thien Minh\} and Lihua Xie",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 ; Conference date: 21-05-2018 Through 25-05-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICRA.2018.8460569",
language = "English",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "836--841",
booktitle = "2018 IEEE International Conference on Robotics and Automation, ICRA 2018",
address = "United States",
}