{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T04:36:21Z","timestamp":1784176581289,"version":"3.55.0"},"reference-count":32,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2024,5,9]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios such as illumination change, rapid rotation and large angle of view variation. In contrast, learning-based keypoints exhibit higher repetition but entail considerable computational costs. This paper proposes an innovative algorithm for keypoint extraction, aiming to strike an equilibrium between precision and efficiency. This paper aims to attain accurate, robust and versatile visual localization in scenes of formidable complexity.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>SiLK-SLAM initially refines the cutting-edge learning-based extractor, SiLK, and introduces an innovative postprocessing algorithm for keypoint homogenization and operational efficiency. Furthermore, SiLK-SLAM devises a reliable relocalization strategy called PCPnP, leveraging progressive and consistent sampling, thereby bolstering its robustness.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Empirical evaluations conducted on TUM, KITTI and EuRoC data sets substantiate SiLK-SLAM\u2019s superior localization accuracy compared to ORB-SLAM3 and other methods. Compared to ORB-SLAM3, SiLK-SLAM demonstrates an enhancement in localization accuracy even by 70.99%, 87.20% and 85.27% across the three data sets. The relocalization experiments demonstrate SiLK-SLAM\u2019s capability in producing precise and repeatable keypoints, showcasing its robustness in challenging environments.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The SiLK-SLAM achieves exceedingly elevated localization accuracy and resilience in formidable scenarios, holding paramount importance in enhancing the autonomy of robots navigating intricate environments. Code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Pepper-FlavoredChewingGum\/SiLK-SLAM\">https:\/\/github.com\/Pepper-FlavoredChewingGum\/SiLK-SLAM<\/jats:ext-link>.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-11-2023-0309","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T23:24:15Z","timestamp":1709853855000},"page":"400-412","source":"Crossref","is-referenced-by-count":4,"title":["SiLK-SLAM: accurate, robust and versatile visual SLAM with simple learned keypoints"],"prefix":"10.1108","volume":"51","author":[{"given":"Jianjun","family":"Yao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingzhao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"key":"key2024050812001596000_ref001","doi-asserted-by":"crossref","unstructured":"Alcantarilla, P.F., Bartoli, A. and Davison, A.J. (2012), \u201cKAZE features\u201d, Paper presented at the European Conference on Computer Vision, Florence, pp. 7-13, doi: 10.1007\/978-3-642-33783-3_16.","DOI":"10.1007\/978-3-642-33783-3_16"},{"key":"key2024050812001596000_ref002","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.5244\/C.27.13","article-title":"Fast explicit diffusion for accelerated features in nonlinear scale spaces","volume-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","year":"2011"},{"key":"key2024050812001596000_ref003","doi-asserted-by":"crossref","unstructured":"Arandjelovi\u0107, R. and Zisserman, A. (2012), \u201cThree things everyone should know to improve object retrieval\u201d, Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Providence, USA, pp. 16-21, doi: 10.1109\/CVPR.2012.6248018.","DOI":"10.1109\/CVPR.2012.6248018"},{"key":"key2024050812001596000_ref004","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1906.02295","article-title":"Progressive NAPSAC: sampling from gradually growing neighborhoods","volume-title":"ArXiv. abs\/1906","year":"2020"},{"issue":"12","key":"key2024050812001596000_ref005","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","article-title":"Surf: speeded-up robust features","volume":"3951","year":"2006","journal-title":"Lecture Notes in Computer Science"},{"issue":"6","key":"key2024050812001596000_ref006","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","article-title":"ORB-SLAM3: an accurate open-source library for visual, visual\u2013inertial, and multimap SLAM","volume":"37","year":"2021","journal-title":"IEEE Transactions on Robotics"},{"key":"key2024050812001596000_ref007","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, P. and Wang, F. (2022), \u201cEPro-PnP: generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation\u201d, Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, pp. 18-24, doi: 10.1109\/CVPR52688.2022.00280.","DOI":"10.1109\/CVPR52688.2022.00280"},{"key":"key2024050812001596000_ref008","doi-asserted-by":"crossref","unstructured":"Chum, O. and Matas, J. (2005), \u201cMatching with PROSAC-progressive sample consensus\u201d, Paper presented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, pp. 20-25, doi: 10.1109\/CVPR.2005.221.","DOI":"10.1109\/CVPR.2005.221"},{"key":"key2024050812001596000_ref009","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T. and Rabinovich, A. (2018), \u201cSuperPoint: self-supervised interest point detection and description\u201d, Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, pp. 18-22, doi: 10.1109\/CVPRW.2018.00060.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"key2024050812001596000_ref010","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Rocco, I. and Pajdla, T. (2019), \u201cD2-Net: a trainable CNN for joint description and detection of local features\u201d, Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, pp. 15-20, doi: 10.1109\/CVPR.2019.00828.","DOI":"10.1109\/CVPR.2019.00828"},{"issue":"3","key":"key2024050812001596000_ref011","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct sparse odometry","volume":"40","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"key2024050812001596000_ref012","doi-asserted-by":"crossref","unstructured":"Gleize, P., Wang, W. and Feiszli, M. (2023), \u201cSiLK - simple learned keypoints\u201d, Paper presented at the IEEE International Conference on Computer Vision, Paris, pp. 2-6, doi: 10.48550\/arXiv.2304.06194.","DOI":"10.1109\/ICCV51070.2023.02056"},{"key":"key2024050812001596000_ref013","doi-asserted-by":"crossref","unstructured":"Harris, C.G. and Stephens, M.J. (1988), A combined corner and edge detector. Paper presented at the Alvey Vision Conference, Manchester.","DOI":"10.5244\/C.2.23"},{"key":"key2024050812001596000_ref014","doi-asserted-by":"crossref","unstructured":"Leutenegger, S., Chli, M. and Siegwart, R.Y. (2011), \u201cBRISK: binary robust invariant scalable keypoints\u201d, Paper presented at the International Conference on Computer Vision, Barcelona, pp. 6-13, doi: 10.1109\/ICCV.2011.6126542.","DOI":"10.1109\/ICCV.2011.6126542"},{"issue":"2","key":"key2024050812001596000_ref015","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale invariant keypoints","volume":"60","year":"2004","journal-title":"International Journal of Computer Vision"},{"issue":"8","key":"key2024050812001596000_ref016","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.automatica.2016.11.009","article-title":"A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems","volume":"77","year":"2017","journal-title":"Automatica"},{"issue":"5","key":"key2024050812001596000_ref017","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras","volume":"33","year":"2017","journal-title":"IEEE Transactions on Robotics"},{"issue":"4","key":"key2024050812001596000_ref018","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","article-title":"VINS-Mono: a robust and versatile monocular visual-inertial state estimator","volume":"34","year":"2018","journal-title":"IEEE Transactions on Robotics"},{"key":"key2024050812001596000_ref019","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V. and Konolige, K. (2011), \u201cORB: an efficient alternative to SIFT or SURF\u201d, Paper presented at the International Conference on Computer Vision, CO Springs, USA, pp. 6-13, doi: 10.1109\/ICCV.2011.6126544.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"key2024050812001596000_ref020","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1607.08112","article-title":"MLPnP - a real-time maximum likelihood solution to the perspective-n-point problem","volume-title":"arXiv. abs\/1607","year":"2016"},{"key":"key2024050812001596000_ref021","doi-asserted-by":"crossref","unstructured":"Sun, J., Shen, Z. and Wang, Y. (2021), \u201cLoFTR: detector-free local feature matching with transformers\u201d, Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, pp. 20-25, doi: 10.1109\/CVPR46437.2021.00881.","DOI":"10.1109\/CVPR46437.2021.00881"},{"issue":"1","key":"key2024050812001596000_ref022","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41074-017-0027-2","article-title":"Visual SLAM algorithms: a survey from 2010 to 2016","volume":"9","year":"2017","journal-title":"IPSJ Transactions on Computer Vision and Applications"},{"issue":"4","key":"key2024050812001596000_ref023","doi-asserted-by":"publisher","first-page":"3505","DOI":"10.1109\/LRA.2019.2927954","article-title":"Gcnv2: efficient correspondence prediction for real-time SLAM","volume":"4","year":"2019","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"7","key":"key2024050812001596000_ref024","first-page":"16558","article-title":"DROID-SLAM: deep visual SLAM for monocular, stereo, and RGB-D cameras","volume":"34","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"key2024050812001596000_ref025","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2208.04726","article-title":"Deep patch visual odometry","volume-title":"arXiv. abs\/2208","year":"2022"},{"issue":"1","key":"key2024050812001596000_ref026","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2006.13566","article-title":"Disk: learning local features with policy gradient","volume":"33","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"key2024050812001596000_ref027","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhang, Y. and Zhu, D. (2020), \u201cEAO-SLAM: monocular semi-dense object SLAM based on ensemble data association\u201d, Paper presented at the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, pp. 24-27, doi: 10.1109\/IROS45743.2020.9341757.","DOI":"10.1109\/IROS45743.2020.9341757"},{"key":"key2024050812001596000_ref028","doi-asserted-by":"crossref","unstructured":"Xu, K., Hao, Y. and Wang, C. (2023), \u201cAirVO: an illumination-robust point-line visual odometry\u201d, Paper presented at the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Detroit, pp. 1-5, doi: 10.1109\/IROS55552.2023.10341914.","DOI":"10.1109\/IROS55552.2023.10341914"},{"key":"key2024050812001596000_ref029","doi-asserted-by":"crossref","unstructured":"Ye, H., Huang, H. and Hutter, M. 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