{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/correlation-flow-robust-optical-flow-using","title":"Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators","arxiv_id":"1802.07078","date":"2018-02-20","proceeding":null,"authors":["Chen Wang","Tete Ji","Thien-Minh Nguyen","Lihua Xie"],"abstract":"Robust velocity and position estimation is crucial for autonomous robot\nnavigation. The optical flow based methods for autonomous navigation have been\nreceiving increasing attentions in tandem with the development of micro\nunmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC)\nbased algorithm to determine optical flow using a monocular camera, which is\nnamed as correlation flow (CF). Correlation flow is able to provide reliable\nand accurate velocity estimation and is robust to motion blur. In addition, it\ncan also estimate the altitude velocity and yaw rate, which are not available\nby traditional methods. Autonomous flight tests on a quadcopter show that\ncorrelation flow can provide robust trajectory estimation with very low\nprocessing power. The source codes are released based on the ROS framework.","url_abs":"http://arxiv.org/abs/1802.07078v2","url_pdf":"http://arxiv.org/pdf/1802.07078v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"correlation-flow-robust-optical-flow-using","repo_url":"https://github.com/wang-chen/correlation_flow","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"correlation-flow-robust-optical-flow-using","repo_url":"https://github.com/sair-lab/ni-slam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":null,"task_name":"Position"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}