{"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/gcnv2-efficient-correspondence-prediction-for","title":"GCNv2: Efficient Correspondence Prediction for Real-Time SLAM","arxiv_id":"1902.11046","date":"2019-02-28","proceeding":null,"authors":["Jiexiong Tang","Ludvig Ericson","John Folkesson","Patric Jensfelt"],"abstract":"In this paper, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORB-SLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone.","url_abs":"https://arxiv.org/abs/1902.11046v3","url_pdf":"https://arxiv.org/pdf/1902.11046v3.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":"gcnv2-efficient-correspondence-prediction-for","repo_url":"https://github.com/jiexiong2016/GCNv2_SLAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"gcnv2-efficient-correspondence-prediction-for","repo_url":"https://github.com/ZKangsen/GCNv2_SLAM_ROS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"gcnv2-efficient-correspondence-prediction-for","repo_url":"https://github.com/gleefe1995/GCNv2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"gcn","method_name":"GCN"},{"method_slug":"orb-slam2","method_name":"ORB-SLAM2"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}