{"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/good-feature-matching-towards-accurate-robust","title":"Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency","arxiv_id":"2001.00714","date":"2020-01-03","proceeding":null,"authors":["Yipu Zhao","Patricio A. Vela"],"abstract":"Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency). Feature-based systems exhibit good performance, yet have higher latency due to explicit data association; direct & semidirect systems have lower latency, but are inapplicable in some target scenarios or exhibit lower accuracy than feature-based ones. This paper aims to fill the performance-efficiency gap with an enhancement applied to feature-based VSLAM. We present good feature matching, an active map-to-frame feature matching method. Feature matching effort is tied to submatrix selection, which has combinatorial time complexity and requires choosing a scoring metric. Via simulation, the Max-logDet matrix revealing metric is shown to perform best. For real-time applicability, the combination of deterministic selection and randomized acceleration is studied. The proposed algorithm is integrated into monocular & stereo feature-based VSLAM systems. Extensive evaluations on multiple benchmarks and compute hardware quantify the latency reduction and the accuracy & robustness preservation.","url_abs":"https://arxiv.org/abs/2001.00714v1","url_pdf":"https://arxiv.org/pdf/2001.00714v1.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":"good-feature-matching-towards-accurate-robust","repo_url":"https://github.com/ivalab/FullResults_GoodFeature","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"good-feature-matching-towards-accurate-robust","repo_url":"https://github.com/ivalab/gf_orb_slam2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"good-feature-matching","method_name":"Good Feature Matching"}],"datasets_introduced":[],"methods_introduced":[{"slug":"good-feature-matching","name":"Good Feature Matching","full_name":"Good Feature Matching"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2001.00714","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}