{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T10:03:35Z","timestamp":1753437815198},"reference-count":49,"publisher":"Informa UK Limited","issue":"5","funder":[{"name":"Higher Education Commission (HEC), Govt. of Pakistan","award":["10023\/Federal\/NRPU\/RD\/HEC\/2017"],"award-info":[{"award-number":["10023\/Federal\/NRPU\/RD\/HEC\/2017"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Advanced Robotics"],"published-print":{"date-parts":[[2024,3,3]]},"DOI":"10.1080\/01691864.2024.2319144","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T18:58:10Z","timestamp":1709146690000},"page":"307-322","update-policy":"http:\/\/dx.doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["One step back, two steps forward: learning moves to recover from SLAM tracking failures"],"prefix":"10.1080","volume":"38","author":[{"given":"Ans","family":"Hussain Qureshi","sequence":"first","affiliation":[{"name":"Centre for Automation and Robotic Engineering Science (CARES), The University of Auckland, Auckland, New Zealand"}]},{"given":"Muhammmad","family":"Latif Anjum","sequence":"additional","affiliation":[{"name":"Robotics & Machine Intelligence (ROMI) Lab, School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan"}]},{"given":"Wajahat","family":"Hussain","sequence":"additional","affiliation":[{"name":"Robotics & Machine Intelligence (ROMI) Lab, School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan"}]},{"given":"Usama","family":"Muddassar","sequence":"additional","affiliation":[{"name":"Robotics & Machine Intelligence (ROMI) Lab, School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan"}]},{"given":"Sohail","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Robotics & Machine Intelligence (ROMI) Lab, School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan"}]}],"member":"301","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2009.5354121"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.456"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364915587924"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-022-10046-9"},{"key":"e_1_3_3_6_1","doi-asserted-by":"crossref","unstructured":"Saxena DM Kurtz V Hebert M. Learning robust failure response for autonomous vision based flight. In: ICRA; IEEE; 2017. p. 5824\u20135829.","DOI":"10.1109\/ICRA.2017.7989684"},{"key":"e_1_3_3_7_1","doi-asserted-by":"crossref","unstructured":"Prasad V Yadav K Saurabh RS et\u00a0al. Learning to prevent monocular slam failure using reinforcement learning. In: Proceedings of the 11th Indian Conference on Computer Vision Graphics and Image Processing; 2018. p. 1\u20139.","DOI":"10.1145\/3293353.3293400"},{"key":"e_1_3_3_8_1","doi-asserted-by":"crossref","unstructured":"Mostegel C Wendel A Bischof H. Active monocular localization: Towards autonomous monocular exploration for multirotor mavs. In: ICRA; IEEE; 2014. p. 3848\u20133855.","DOI":"10.1109\/ICRA.2014.6907417"},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"Geiger A Lenz P Urtasun R. Are we ready for autonomous driving? the Kitti vision benchmark suite. In: CVPR; IEEE; 2012. p. 3354\u20133361.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"e_1_3_3_10_1","doi-asserted-by":"crossref","unstructured":"Sturm J Engelhard N Endres F et al. A benchmark for the evaluation of RGB-D SLAM systems. In: 2012 IEEE\/RSJ international conference on intelligent robots and systems. IEEE; 2012. p. 573-580.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364909103911"},{"key":"e_1_3_3_12_1","doi-asserted-by":"crossref","unstructured":"Wang W Zhu D Wang X et\u00a0al. Tartanair: A dataset to push the limits of visual slam. In: IROS; IEEE; 2020.","DOI":"10.1109\/IROS45743.2020.9341801"},{"key":"e_1_3_3_13_1","doi-asserted-by":"crossref","unstructured":"Torralba A Efros AA. Unbiased look at dataset bias. In: CVPR; IEEE; 2011. p. 1521\u20131528.","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_3_3_14_1","unstructured":"Savva M Chang AX Dosovitskiy A et\u00a0al. MINOS: Multimodal indoor simulator for navigation in complex environments. arXiv:171203931. 2017."},{"key":"e_1_3_3_15_1","doi-asserted-by":"crossref","unstructured":"Ammirato P Poirson P Park E et\u00a0al. A dataset for developing and benchmarking active vision. In: ICRA; IEEE; 2017. p. 1378\u20131385.","DOI":"10.1109\/ICRA.2017.7989164"},{"key":"e_1_3_3_16_1","doi-asserted-by":"crossref","unstructured":"Hartmann W Havlena M Schindler K. Predicting matchability. In: CVPR; 2014. p. 9\u201316.","DOI":"10.1109\/CVPR.2014.9"},{"key":"e_1_3_3_17_1","doi-asserted-by":"crossref","unstructured":"Daftry S Zeng S Bagnell JA et\u00a0al. Introspective perception: Learning to predict failures in vision systems. In: IROS; IEEE; 2016. p. 1743\u20131750.","DOI":"10.1109\/IROS.2016.7759279"},{"key":"e_1_3_3_18_1","unstructured":"Rabiee S Biswas J. IV-SLAM: Introspective vision for simultaneous localization and mapping. In: Conference on Robot Learning. PMLR; 2021. p. 1100-1109."},{"key":"e_1_3_3_19_1","doi-asserted-by":"crossref","unstructured":"Deng X Zhang Z Sintov A et\u00a0al. Feature-constrained active visual slam for mobile robot navigation. In: ICRA; IEEE; 2018. p. 7233\u20137238.","DOI":"10.1109\/ICRA.2018.8460721"},{"key":"e_1_3_3_20_1","doi-asserted-by":"crossref","unstructured":"Michels J Saxena A Ng AY. High speed obstacle avoidance using monocular vision and reinforcement learning. In: Proceedings of the 22nd international conference on Machine learning; 2005. p. 593\u2013600.","DOI":"10.1145\/1102351.1102426"},{"key":"e_1_3_3_21_1","doi-asserted-by":"crossref","unstructured":"Savva M Kadian A Maksymets O et al. Habitat: A platform for embodied ai research. In: Proceedings of the IEEE\/CVF international conference on computer vision; 2019. p. 9339\u20139347.","DOI":"10.1109\/ICCV.2019.00943"},{"key":"e_1_3_3_22_1","unstructured":"Mishkin D Dosovitskiy A Koltun V. Benchmarking classic and learned navigation in complex 3D environments. arXiv preprint arXiv:190110915. 2019."},{"key":"e_1_3_3_23_1","unstructured":"Bhatti S Desmaison A Miksik O et\u00a0al. Playing doom with slam-augmented deep reinforcement learning. arXiv preprint arXiv:161200380. 2016."},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016"},{"key":"e_1_3_3_25_1","article-title":"Generative adversarial imitation learning","volume":"29","author":"Ho J","year":"2016","unstructured":"Ho J, Ermon S. Generative adversarial imitation learning. Adv Neural Inform Proces Syst. 2016;29.","journal-title":"Adv Neural Inform Proces Syst"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109621"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2017.2705103"},{"key":"e_1_3_3_28_1","unstructured":"Schubert S Lange S Neubert P et\u00a0al. Map enhancement with track-loss data in visual slam. 2016."},{"key":"e_1_3_3_29_1","doi-asserted-by":"crossref","unstructured":"Klein G Murray D. Parallel tracking and mapping for small ar workspaces. In: ISMAR; IEEE; 2007. p. 225\u2013234.","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2015.2463671"},{"key":"e_1_3_3_31_1","doi-asserted-by":"crossref","unstructured":"Elvira R Tard\u00f3s JD Montiel J. Orbslam-atlas: a robust and accurate multi-map system. arXiv preprint arXiv:190811585. 2019.","DOI":"10.1109\/IROS40897.2019.8967572"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3075644"},{"key":"e_1_3_3_33_1","doi-asserted-by":"crossref","unstructured":"Pascoe G Maddern W Tanner M et al. Nid-slam: robust monocular slam using normalised information distance. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 1435\u20131444.","DOI":"10.1109\/CVPR.2017.158"},{"key":"e_1_3_3_34_1","doi-asserted-by":"crossref","unstructured":"Tateno K Tombari F Laina I et al. Cnn-slam: real-time dense monocular slam with learned depth prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 6243\u20136252.","DOI":"10.1109\/CVPR.2017.695"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.8860"},{"key":"e_1_3_3_36_1","doi-asserted-by":"crossref","unstructured":"Gao X Wang R Demmel N et\u00a0al. Ldso: direct sparse odometry with loop closure. In: IROS; IEEE; 2018. p. 2198\u20132204.","DOI":"10.1109\/IROS.2018.8593376"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.8860"},{"key":"e_1_3_3_38_1","first-page":"16558","article-title":"Droid-slam: deep visual slam for monocular, stereo, and rgb-d cameras","volume":"34","author":"Teed Z","year":"2021","unstructured":"Teed Z, Deng J. Droid-slam: deep visual slam for monocular, stereo, and rgb-d cameras. Adv Neural Inf Process Syst. 2021;34:16558\u201316569.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-023-10149-x"},{"key":"e_1_3_3_40_1","first-page":"251","article-title":"Habitat 2.0: training home assistants to rearrange their habitat","volume":"34","author":"Szot A","year":"2021","unstructured":"Szot A, Clegg A, Undersander E, et\u00a0al. Habitat 2.0: training home assistants to rearrange their habitat. Adv Neural Inform Proces Syst. 2021;34:251\u2013266.","journal-title":"Adv Neural Inform Proces Syst"},{"key":"e_1_3_3_41_1","unstructured":"Dosovitskiy A Ros G Codevilla F et\u00a0al. Carla: an open urban driving simulator. In: CoRL; PMLR; 2017. p. 1\u201316."},{"key":"e_1_3_3_42_1","doi-asserted-by":"crossref","unstructured":"Shah S Dey D Lovett C et al. Airsim: high-fidelity visual and physical simulation for autonomous vehicles. In: Field and service robotics: results of the 11th International Conference. Springer International Publishing; 2018. p. 621\u2013635.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"e_1_3_3_43_1","doi-asserted-by":"crossref","unstructured":"Concha A Hussain MW Montano L et\u00a0al. Manhattan and piecewise-planar constraints for dense monocular mapping. In: Robotics: Science and Systems; 2014.","DOI":"10.15607\/RSS.2014.X.016"},{"key":"e_1_3_3_44_1","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_45_1","doi-asserted-by":"crossref","unstructured":"Henriques JF Vedaldi A. Mapnet: an allocentric spatial memory for mapping environments. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. p. 8476\u20138484.","DOI":"10.1109\/CVPR.2018.00884"},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2623335"},{"key":"e_1_3_3_47_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2007.09.014"},{"key":"e_1_3_3_49_1","doi-asserted-by":"crossref","unstructured":"Rublee E Rabaud V Konolige K et al. ORB: an efficient alternative to sift or surf. In: 2011 International conference on computer vision; IEEE; 2011. p. 2564\u20132571.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"e_1_3_3_50_1","doi-asserted-by":"crossref","unstructured":"Rosten E Drummond T. Machine learning for high-speed corner detection. In: ECCV; Springer; 2006. p. 430\u2013443.","DOI":"10.1007\/11744023_34"}],"container-title":["Advanced Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/01691864.2024.2319144","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T15:47:37Z","timestamp":1727192857000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/01691864.2024.2319144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":49,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,3,3]]}},"alternative-id":["10.1080\/01691864.2024.2319144"],"URL":"https:\/\/doi.org\/10.1080\/01691864.2024.2319144","relation":{},"ISSN":["0169-1864","1568-5535"],"issn-type":[{"type":"print","value":"0169-1864"},{"type":"electronic","value":"1568-5535"}],"subject":[],"published":{"date-parts":[[2024,2,28]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tadr20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tadr20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2023-05-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-18","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}