{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:07:37Z","timestamp":1772766457153,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030642204","type":"print"},{"value":"9783030642211","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"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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\/978-3-030-64221-1_14","type":"book-chapter","created":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T11:02:33Z","timestamp":1606561353000},"page":"154-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Point Cloud Odometry: A Deep Learning Based Odometry with 3D Laser Point Clouds"],"prefix":"10.1007","author":[{"given":"Chi","family":"Li","sequence":"first","affiliation":[]},{"given":"Yisha","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Zhuang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586\u2013606. International Society for Optics and Photonics (1992)","DOI":"10.1117\/12.57955"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Cho, Y., Kim, G., Kim, A.: DeepLO: geometry-aware deep lidar odometry. arXiv preprint arXiv:1902.10562 (2019)","DOI":"10.1109\/ICRA40945.2020.9197366"},{"key":"14_CR3","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"14_CR4","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 (CVPR), pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"14_CR5","unstructured":"Grupp, M.: EVO: Python package for the evaluation of odometry and SLAM (2017). https:\/\/github.com\/MichaelGrupp\/evo"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Kendall, A., Cipolla, R.: Geometric loss functions for camera pose regression with deep learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6555\u20136564 (2017)","DOI":"10.1109\/CVPR.2017.694"},{"key":"14_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Li, J., Zhan, H., Chen, B.M., Reid, I., Lee, G.H.: Deep learning for 2D scan matching and loop closure. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 763\u2013768. IEEE (2017)","DOI":"10.1109\/IROS.2017.8202236"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Li, Q., Chen, S., Wang, C., Li, X., Wen, C., Cheng, M., Li, J.: LO-Net: deep real-time lidar odometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8473\u20138482 (2019)","DOI":"10.1109\/CVPR.2019.00867"},{"key":"14_CR10","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":"14_CR11","unstructured":"Magnusson, M.: The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection. Ph.D. thesis, \u00d6rebro universitet (2009)"},{"key":"14_CR12","unstructured":"Nicolai, A., Skeele, R., Eriksen, C., Hollinger, G.A.: Deep learning for laser based odometry estimation. In: RSS workshop Limits and Potentials of Deep Learning in Robotics, vol. 184 (2016)"},{"key":"14_CR13","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026\u20138037 (2019)"},{"key":"14_CR14","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652\u2013660 (2017)"},{"key":"14_CR15","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099\u20135108 (2017)"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Robotics: Science and Systems, Seattle, WA , vol. 2, p. 435 (2009)","DOI":"10.15607\/RSS.2009.V.021"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Serafin, J., Grisetti, G.: NICP: dense normal based point cloud registration. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 742\u2013749. IEEE (2015)","DOI":"10.1109\/IROS.2015.7353455"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Shan, T., Englot, B.: LeGO-LOAM: lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4758\u20134765. IEEE (2018)","DOI":"10.1109\/IROS.2018.8594299"},{"issue":"12","key":"14_CR19","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1177\/0278364912460895","volume":"31","author":"T Stoyanov","year":"2012","unstructured":"Stoyanov, T., Magnusson, M., Andreasson, H., Lilienthal, A.J.: Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations. Int. J. Robot. Res. 31(12), 1377\u20131393 (2012)","journal-title":"Int. J. Robot. Res."},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Velas, M., Spanel, M., Hradis, M., Herout, A.: CNN for IMU assisted odometry estimation using velodyne LiDAR. In: 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 71\u201377. IEEE (2018)","DOI":"10.1109\/ICARSC.2018.8374163"},{"issue":"4\u20135","key":"14_CR21","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. Int. J. Robot. Res. 37(4\u20135), 513\u2013542 (2018)","journal-title":"Int. J. Robot. Res."},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Wong, J.M., et al.: SegICP: integrated deep semantic segmentation and pose estimation. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5784\u20135789. IEEE (2017)","DOI":"10.1109\/IROS.2017.8206470"},{"key":"14_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1007\/978-3-319-46475-6_47","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Q-Y Zhou","year":"2016","unstructured":"Zhou, Q.-Y., Park, J., Koltun, V.: Fast global registration. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 766\u2013782. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_47"},{"key":"14_CR24","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 (CVPR), pp. 1851\u20131858 (2017)","DOI":"10.1109\/CVPR.2017.700"}],"container-title":["Lecture Notes in Computer Science","Advances in Neural Networks \u2013 ISNN 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64221-1_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T11:07:47Z","timestamp":1606561667000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-64221-1_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030642204","9783030642211"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64221-1_14","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":"27 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISNN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cairo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Egypt","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isnn2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/isnn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"39","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":"24","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":"62% - 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":"3","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)"}}]}}