{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:11:26Z","timestamp":1774667486107,"version":"3.50.1"},"reference-count":24,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/lra.2018.2869640","type":"journal-article","created":{"date-parts":[[2018,9,13]],"date-time":"2018-09-13T21:16:03Z","timestamp":1536873363000},"page":"4407-4414","source":"Crossref","is-referenced-by-count":206,"title":["VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry"],"prefix":"10.1109","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1405-845X","authenticated-orcid":false,"given":"Noha","family":"Radwan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4710-3114","authenticated-orcid":false,"given":"Abhinav","family":"Valada","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5680-6500","authenticated-orcid":false,"given":"Wolfram","family":"Burgard","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"2938","article-title":"PoseNet: A\n convolutional network for real-time 6-DOF camera relocalization","author":"kendall","year":"0","journal-title":"Proc Int Conf Comput Vis"},{"key":"ref11","article-title":"Camera relocalization by computing\n pairwise relative poses","author":"laskar","year":"2017"},{"key":"ref12","article-title":"Learning less is more&#x2014;6D camera localization via 3D surface regression","author":"brachmann","year":"2017"},{"key":"ref13","article-title":"Deep learning for laser based odometry\n estimation","author":"nicolai","year":"0","journal-title":"RSSws Limits and Potentials of Deep Learning in Robotics"},{"key":"ref14","article-title":"DeepVO: A deep learning approach for monocular\n visual odometry","author":"mohanty","year":"2016"},{"key":"ref15","first-page":"3431","article-title":"Fully\n convolutional networks for semantic segmentation","author":"long","year":"0","journal-title":"Proc Comput Vis Pattern Recognit"},{"key":"ref16","first-page":"1634","article-title":"Deep learning for human part discovery in images","author":"oliveira","year":"0","journal-title":"Proc Int Conf Robot Automat"},{"key":"ref17","article-title":"SegNet: A\n deep convolutional encoder-decoder architecture","author":"badrinarayanan","year":"2015"},{"key":"ref18","article-title":"ParseNet: Looking wider\n to see better","author":"liu","year":"2015"},{"key":"ref19","article-title":"Semantic image segmentation with deep\n convolutional nets, atrous convolution, and CRFs","author":"chen","year":"2016"},{"key":"ref4","article-title":"Deep regression for monocular camera-based 6-DoF global localization in outdoor environments","author":"naseer","year":"0","journal-title":"Proc IEEE\/RSJ Int Conf Intell Robots Syst"},{"key":"ref3","article-title":"Geometric loss functions for camera pose regression with deep learning","volume":"3","author":"kendall","year":"0","journal-title":"Proc Comput Vis Pattern Recognit"},{"key":"ref6","article-title":"Universal representations: The missing link between faces, text, planktons, and cat breeds","author":"bilen","year":"2017"},{"key":"ref5","article-title":"Deep\n auxiliary learning for visual localization and odometry","author":"valada","year":"0","journal-title":"Proc Int Conf Robot Automat"},{"key":"ref8","article-title":"Multi-task learning using uncertainty to weigh\n losses for scene geometry and semantics","author":"kendall","year":"2017"},{"key":"ref7","article-title":"Outrageously large neural networks: The\n sparsely-gated mixture-of-experts","author":"shazeer","year":"2017"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-70353-4_57","article-title":"Relative\n camera pose estimation using convolutional neural networks","author":"melekhov","year":"2017"},{"key":"ref1","first-page":"4644","article-title":"AdapNet: Adaptive semantic segmentation\n in adverse environmental conditions","author":"valada","year":"0","journal-title":"Proc Int Conf Robot Automat"},{"key":"ref9","article-title":"DSAC&#x2014;Differentiable RANSAC for camera\n localization","volume":"3","author":"brachmann","year":"0","journal-title":"Proc Comput Vis Pattern Recognit"},{"key":"ref20","first-page":"2930","article-title":"Scene coordinate regression forests for camera relocalization in RGB-D images","author":"shotton","year":"0","journal-title":"Proc IEEE Comput Vis Pattern Recognit"},{"key":"ref22","article-title":"Modelling uncertainty in deep learning for camera relocalization","author":"kendall","year":"0","journal-title":"Proc Int Conf Robot Automat"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21534"},{"key":"ref24","article-title":"Grad-CAM++: Generalized gradient-based visual\n explanations for deep convolutional networks","author":"chattopadhyay","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2477680"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/8386768\/08458420.pdf?arnumber=8458420","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T10:50:46Z","timestamp":1643194246000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8458420\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":24,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/lra.2018.2869640","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10]]}}}