{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:28:09Z","timestamp":1778606889714,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030414030","type":"print"},{"value":"9783030414047","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41404-7_26","type":"book-chapter","created":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T07:02:58Z","timestamp":1582354978000},"page":"366-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optical Flow Assisted Monocular Visual Odometry"],"prefix":"10.1007","author":[{"given":"Yiming","family":"Wan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"issue":"3","key":"26_CR1","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1109\/LRA.2018.2803211","volume":"3","author":"Gabriele Costante","year":"2018","unstructured":"Costante, G., Ciarfuglia, T.A.: Ls-vo: Learning dense optical subspace for robust visual odometry estimation. IEEE Robotics and Automation Letters 3(3), 1735\u20131742 (2018)","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"1","key":"26_CR2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/LRA.2015.2505717","volume":"1","author":"G Costante","year":"2016","unstructured":"Costante, G., Mancini, M., Valigi, P., Ciarfuglia, T.A.: Exploring representation learning with cnns for frame-to-frame ego-motion estimation. IEEE robotics and automation letters 1(1), 18\u201325 (2016)","journal-title":"IEEE robotics and automation letters"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera. In: Proceedings of the IEEE international conference on computer vision. pp. 1449\u20131456 (2013)","DOI":"10.1109\/ICCV.2013.183"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: Dense 3d reconstruction in real-time. In: 2011 IEEE Intelligent Vehicles Symposium (IV). pp. 963\u2013968. Ieee (2011)","DOI":"10.1109\/IVS.2011.5940405"},{"issue":"8","key":"26_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735\u20131780 (1997)","journal-title":"Neural computation"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Iyer, G., Krishna Murthy, J., Gupta, G., Krishna, M., Paull, L.: Geometric consistency for self-supervised end-to-end visual odometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 267\u2013275 (2018)","DOI":"10.1109\/CVPRW.2018.00064"},{"key":"26_CR9","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in neural information processing systems. pp. 2017\u20132025 (2015)"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Jason, J.Y., Harley, A.W., Derpanis, K.G.: Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness. In: European Conference on Computer Vision. pp. 3\u201310. Springer (2016)","DOI":"10.1007\/978-3-319-49409-8_1"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Kendall, A., Cipolla, R.: Geometric loss functions for camera pose regression with deep learning. computer vision and pattern recognition pp. 6555\u20136564 (2017)","DOI":"10.1109\/CVPR.2017.694"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Konda, K.R., Memisevic, R.: Learning visual odometry with a convolutional network. In: VISAPP (1). pp. 486\u2013490 (2015)","DOI":"10.5220\/0005299304860490"},{"key":"26_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097\u20131105 (2012)"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, S., Long, Z., Gu, D.: Undeepvo: Monocular visual odometry through unsupervised deep learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). pp. 7286\u20137291. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8461251"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Mahjourian, R., Wicke, M., Angelova, A.: Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5667\u20135675 (2018)","DOI":"10.1109\/CVPR.2018.00594"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Meister, S., Hur, J., Roth, S.: Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12276"},{"key":"26_CR17","unstructured":"Mohanty, V., Agrawal, S., Datta, S., Ghosh, A., Sharma, V.D., Chakravarty, D.: Deepvo: A deep learning approach for monocular visual odometry. arXiv preprint arXiv:1611.06069 (2016)"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Muller, P., Savakis, A.: Flowdometry: An optical flow and deep learning based approach to visual odometry. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 624\u2013631. IEEE (2017)","DOI":"10.1109\/WACV.2017.75"},{"issue":"5","key":"26_CR19","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","volume":"31","author":"R Murartal","year":"2015","unstructured":"Murartal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: A versatile and accurate monocular slam system. IEEE Transactions on Robotics 31(5), 1147\u20131163 (2015)","journal-title":"IEEE Transactions on Robotics"},{"key":"26_CR20","unstructured":"Vijayanarasimhan, S., Ricco, S., Schmid, C., Sukthankar, R., Fragkiadaki, K.: Sfm-net: Learning of structure and motion from video. arXiv preprint arXiv:1704.07804 (2017)"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Wang, S., Clark, R., Wen, H., Trigoni, N.: Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). pp. 2043\u20132050. IEEE (2017)","DOI":"10.1109\/ICRA.2017.7989236"},{"issue":"4\u20135","key":"26_CR22","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1177\/0278364917734298","volume":"37","author":"S Wang","year":"2018","unstructured":"Wang, S., Clark, R., Wen, H., Trigoni, N.: End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks. The International Journal of Robotics Research 37(4\u20135), 513\u2013542 (2018)","journal-title":"The International Journal of Robotics Research"},{"key":"26_CR23","unstructured":"Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems. pp. 802\u2013810 (2015)"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Zhan, H., Garg, R., Saroj Weerasekera, C., Li, K., Agarwal, H., Reid, I.: Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 340\u2013349 (2018)","DOI":"10.1109\/CVPR.2018.00043"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1851\u20131858 (2017)","DOI":"10.1109\/CVPR.2017.700"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41404-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T04:19:50Z","timestamp":1665893990000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41404-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030414030","9783030414047"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41404-7_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"214","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"for ACPR 2019 Workshops volume accepted 17 full papers and 6 short papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}