{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:25:07Z","timestamp":1766067907075,"version":"3.37.3"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Robot."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tro.2024.3351548","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T20:56:38Z","timestamp":1704833798000},"page":"1373-1392","source":"Crossref","is-referenced-by-count":9,"title":["Uni-Fusion: Universal Continuous Mapping"],"prefix":"10.1109","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6147-4846","authenticated-orcid":false,"given":"Yijun","family":"Yuan","sequence":"first","affiliation":[{"name":"Informatics XVII – Robotics, Julius-Maximilians-University of Würzburg, Wurzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3870-783X","authenticated-orcid":false,"given":"Andreas","family":"N\u00fcchter","sequence":"additional","affiliation":[{"name":"Informatics XVII – Robotics, Julius-Maximilians-University of Würzburg, Wurzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911421039"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2013.6697034"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-017-9668-3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2018.8581356"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2016.2631260"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794324"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3061356"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3189434"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/237170.237269"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2047196.2047270"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3054739"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00882"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3185383"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00617"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01245"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/37402.37422"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00607"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IROS55552.2023.10341922"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20059-5_31"},{"key":"ref20","first-page":"458","article-title":"Bayesian hilbert maps for dynamic continuous occupancy mapping","volume-title":"Proc. Conf. Robot Learn.","author":"Senanayake","year":"2017"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793508"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00025"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00459"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_36"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00604"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_31"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/3DV53792.2021.00135"},{"key":"ref28","first-page":"4976","article-title":"NEURALEF: Deconstructing kernels by deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Deng","year":"2022"},{"key":"ref29","first-page":"1","article-title":"Random features for large-scale kernel machines","volume":"20","author":"Rahimi","year":"2007","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref30","first-page":"1","article-title":"Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning","volume":"21","author":"Rahimi","year":"2008","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref31","first-page":"1","article-title":"Orthogonal random features","volume":"29","author":"Yu","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref32","first-page":"1","article-title":"Quadrature-based features for kernel approximation","volume":"31","author":"Munkhoeva","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09880-z"},{"key":"ref34","first-page":"1","article-title":"Using the nystrm method to speed up kernel machines","volume":"13","author":"Williams","year":"2000","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref35","first-page":"1","article-title":"Nystrm method vs random Fourier features: A theoretical and empirical comparison","volume":"25","author":"Yang","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref36","volume-title":"Gaussian Processes for Machine Learning","volume":"2","author":"Williams","year":"2006"},{"key":"ref37","first-page":"299","article-title":"Classes of kernels for machine learning: A statistics perspective","volume":"2","author":"Genton","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref38","first-page":"1","article-title":"Derivative observations in Gaussian process models of dynamic systems","volume":"15","author":"Solak","year":"2002","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.25"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00085"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2023.xix.074"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160969"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11018-5_32"},{"key":"ref46","first-page":"1","article-title":"PointNet : Deep hierarchical feature learning on point sets in a metric space","volume":"30","author":"Qi","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.170"},{"year":"2016","key":"ref48","article-title":"Occipital: The structure sensor"},{"article-title":"Joint 2D-3D-semantic data for indoor scene understanding","year":"2017","author":"Armeni","key":"ref49"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00311"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.23919\/MVA.2019.8758063"},{"key":"ref52","first-page":"1","article-title":"Devise: A deep visual-semantic embedding model","volume":"26","author":"Frome","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/3DV53792.2021.00107"},{"article-title":"Open3D: A modern library for 3D data processing","year":"2018","author":"Zhou","key":"ref54"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00022"},{"key":"ref56","article-title":"Kintinuous: Spatially extended kinectfusion","volume-title":"Proc. RSS 12 Workshop RGB-D: Adv. Reasoning Depth Cameras","author":"Whelan","year":"2012"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2017.2705103"},{"key":"ref58","first-page":"16 55816 569","article-title":"Droid-SLAM: Deep visual slam for monocular, stereo, and RGB-D cameras","volume":"34","author":"Teed","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530127"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-26293-7_16"}],"container-title":["IEEE Transactions on Robotics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8860\/10365208\/10384743.pdf?arnumber=10384743","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T10:47:36Z","timestamp":1706784456000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10384743\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/tro.2024.3351548","relation":{},"ISSN":["1552-3098","1941-0468"],"issn-type":[{"type":"print","value":"1552-3098"},{"type":"electronic","value":"1941-0468"}],"subject":[],"published":{"date-parts":[[2024]]}}}