{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T19:09:10Z","timestamp":1767035350362,"version":"3.28.0"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000028","name":"Semiconductor Research Corporation (SRC)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,1]]},"DOI":"10.1109\/iros55552.2023.10341924","type":"proceedings-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T14:17:55Z","timestamp":1702477075000},"page":"7742-7749","source":"Crossref","is-referenced-by-count":11,"title":["Lightweight, Uncertainty-Aware Conformalized Visual Odometry"],"prefix":"10.1109","author":[{"given":"Alex C.","family":"Stutts","sequence":"first","affiliation":[{"name":"University of Illinois Chicago (UIC),Chicago,IL"}]},{"given":"Danilo","family":"Erricolo","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago (UIC),Chicago,IL"}]},{"given":"Theja","family":"Tulabandhula","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago (UIC),Chicago,IL"}]},{"given":"Amit Ranjan","family":"Trivedi","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago (UIC),Chicago,IL"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/b106715"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1307116"},{"key":"ref4","first-page":"371","article-title":"A Tutorial on Conformal Prediction","volume":"9","author":"Shafer","year":"2008","journal-title":"J. Mach. Learn. Res."},{"volume-title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","year":"2021","author":"Angelopoulos","key":"ref5"},{"key":"ref6","article-title":"Training uncertainty-aware classifiers with conformalized deep learning","author":"Einbinder","year":"2022","journal-title":"NeurIPS"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50146-4_39"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2011.943233"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2016.2635686"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48036-7_62"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2015.7225730"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.336"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.75"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989236"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461251"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096758"},{"key":"ref17","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","volume-title":"International Cnference on Machine Learning","author":"Gal","year":"2016"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2020.3001674"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00136"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636827"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892795"},{"volume-title":"Conformalized Quantile Regression","year":"2019","author":"Romano","key":"ref22"},{"issue":"24","key":"ref23","first-page":"1","article-title":"Calibrated multiple-output quantile regression with representation learning","volume":"24","author":"Feldman","year":"2023","journal-title":"Journal of Machine Learning Research"},{"volume-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","year":"2017","author":"Howard","key":"ref24"},{"key":"ref25","article-title":"Beyond pin-ball loss: Quantile methods for calibrated uncertainty quantification","volume":"34","author":"Chung","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001437"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1972.10481224"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-013-9368-4"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907298"}],"event":{"name":"2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","start":{"date-parts":[[2023,10,1]]},"location":"Detroit, MI, USA","end":{"date-parts":[[2023,10,5]]}},"container-title":["2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10341341\/10341342\/10341924.pdf?arnumber=10341924","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T19:26:14Z","timestamp":1703013974000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10341924\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,1]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/iros55552.2023.10341924","relation":{},"subject":[],"published":{"date-parts":[[2023,10,1]]}}}