{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:50:47Z","timestamp":1770749447632,"version":"3.50.0"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030880064","type":"print"},{"value":"9783030880071","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-88007-1_8","type":"book-chapter","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:06:25Z","timestamp":1634857585000},"page":"91-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder"],"prefix":"10.1007","author":[{"given":"Zhenyu","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qieshi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fusheng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziliang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"8_CR1","unstructured":"Glover, A.: Gardens point walking (2014). https:\/\/wiki.qut.edu.au\/display\/raq\/day+and+night+with+lateral+pose+change+datasets"},{"key":"8_CR2","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io\/"},{"key":"8_CR3","unstructured":"ImageNet, an image database organized according to the wordnet hierarchy. http:\/\/www.image-net.org\/"},{"key":"8_CR4","unstructured":"Babenko, A., Lempitsky, V.: Aggregating local deep features for image retrieval. In: IEEE International Conference on Computer Vision (ICCV), pp. 1269\u20131277 (2015)"},{"issue":"1","key":"8_CR5","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1177\/0278364917740639","volume":"37","author":"L Bampis","year":"2018","unstructured":"Bampis, L., Amanatiadis, A., Gasteratos, A.: Fast loop-closure detection using visual-word-vectors from image sequences. Int. J. Robot. Res. (IJRR) 37(1), 62\u201382 (2018)","journal-title":"Int. J. Robot. Res. (IJRR)"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Camara, L.G., G\u00e4bert, C., P\u0159eu\u010dil, L.: Highly robust visual place recognition through spatial matching of CNN features. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3748\u20133755 (2020)","DOI":"10.1109\/ICRA40945.2020.9196967"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Deep learning features at scale for visual place recognition. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3223\u20133230 (2017)","DOI":"10.1109\/ICRA.2017.7989366"},{"key":"8_CR8","unstructured":"Chen, Z., Lam, O., Jacobson, A., Milford, M.: Convolutional neural network-based place recognition. arXiv preprint arXiv:1411.1509 (2014)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Maffra, F., Sa, I., Chli, M.: Only look once, mining distinctive landmarks from convnet for visual place recognition. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9\u201316 (2017)","DOI":"10.1109\/IROS.2017.8202131"},{"issue":"6","key":"8_CR10","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1177\/0278364908090961","volume":"27","author":"M Cummins","year":"2008","unstructured":"Cummins, M., Newman, P.: FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. (IJRR) 27(6), 647\u2013665 (2008)","journal-title":"Int. J. Robot. Res. (IJRR)"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886\u2013893 (2005)","DOI":"10.1109\/CVPR.2005.177"},{"issue":"5","key":"8_CR12","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/TRO.2012.2197158","volume":"28","author":"D G\u00e1lvez-L\u00f3pez","year":"2012","unstructured":"G\u00e1lvez-L\u00f3pez, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. (TRO) 28(5), 1188\u20131197 (2012)","journal-title":"IEEE Trans. Robot. (TRO)"},{"issue":"1","key":"8_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10514-015-9516-2","volume":"41","author":"X Gao","year":"2015","unstructured":"Gao, X., Zhang, T.: Unsupervised learning to detect loops using deep neural networks for visual SLAM system. Auton. Robot. 41(1), 1\u201318 (2015). https:\/\/doi.org\/10.1007\/s10514-015-9516-2","journal-title":"Auton. Robot."},{"key":"8_CR14","unstructured":"Hausler, S., Jacobson, A., Milford, M.: Feature map filtering: improving visual place recognition with convolutional calibration. arXiv preprint arXiv:1810.12465 (2018)"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"7702","DOI":"10.1109\/ACCESS.2017.2698524","volume":"5","author":"Y Hou","year":"2017","unstructured":"Hou, Y., Zhang, H., Zhou, S., Zou, H.: Use of roadway scene semantic information and geometry-preserving landmark pairs to improve visual place recognition in changing environments. IEEE Access 5, 7702\u20137713 (2017)","journal-title":"IEEE Access"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.patrec.2017.10.028","volume":"100","author":"C Kenshimov","year":"2017","unstructured":"Kenshimov, C., Bampis, L., Amirgaliyev, B., Arslanov, M., Gasteratos, A.: Deep learning features exception for cross-season visual place recognition. Pattern Recognit. Lett. (PRL) 100, 124\u2013130 (2017)","journal-title":"Pattern Recognit. Lett. (PRL)"},{"issue":"2","key":"8_CR17","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TRO.2019.2956352","volume":"36","author":"A Khaliq","year":"2019","unstructured":"Khaliq, A., Ehsan, S., Chen, Z., Milford, M., McDonald-Maier, K.: A holistic visual place recognition approach using lightweight CNNs for significant viewpoint and appearance changes. IEEE Trans. Robot. (TRO) 36(2), 561\u2013569 (2019)","journal-title":"IEEE Trans. Robot. (TRO)"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Labbe, M., Michaud, F.: Online global loop closure detection for large-scale multi-session graph-based SLAM. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2661\u20132666 (2014)","DOI":"10.1109\/IROS.2014.6942926"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Liu, L., Shen, C., van den Hengel, A.: The treasure beneath convolutional layers: cross-convolutional-layer pooling for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4749\u20134757 (2015)","DOI":"10.1109\/CVPR.2015.7299107"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Xiang, R., Zhang, Q., Ren, Z., Cheng, J.: Loop closure detection based on improved hybrid deep learning architecture. In: IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), pp. 312\u2013317 (2019)","DOI":"10.1109\/IUCC\/DSCI\/SmartCNS.2019.00079"},{"key":"8_CR21","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.patrec.2017.04.017","volume":"92","author":"M Lopez-Antequera","year":"2017","unstructured":"Lopez-Antequera, M., Gomez-Ojeda, R., Petkov, N., Gonzalez-Jimenez, J.: Appearance-invariant place recognition by discriminatively training a convolutional neural network. Pattern Recogn. Lett. (PRL) 92, 89\u201395 (2017)","journal-title":"Pattern Recogn. Lett. (PRL)"},{"issue":"2","key":"8_CR22","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. (IJCV) 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"issue":"1","key":"8_CR23","first-page":"1","volume":"32","author":"S Lowry","year":"2015","unstructured":"Lowry, S., et al.: Visual place recognition: a survey. IEEE Trans. Robot. (TRO) 32(1), 1\u201319 (2015)","journal-title":"IEEE Trans. Robot. (TRO)"},{"issue":"2","key":"8_CR24","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1109\/LRA.2019.2895826","volume":"4","author":"F Maffra","year":"2019","unstructured":"Maffra, F., Teixeira, L., Chen, Z., Chli, M.: Real-time wide-baseline place recognition using depth completion. IEEE Robot. Autom. Lett. (RAL) 4(2), 1525\u20131532 (2019)","journal-title":"IEEE Robot. Autom. Lett. (RAL)"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Merrill, N., Huang, G.: Lightweight unsupervised deep loop closure. arXiv preprint arXiv:1805.07703 (2018)","DOI":"10.15607\/RSS.2018.XIV.032"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Milford, M.J., Wyeth, G.F.: SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1643\u20131649 (2012)","DOI":"10.1109\/ICRA.2012.6224623"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Naseer, T., Ruhnke, M., Stachniss, C., Spinello, L., Burgard, W.: Robust visual SLAM across seasons. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2529\u20132535 (2015)","DOI":"10.1109\/IROS.2015.7353721"},{"key":"8_CR28","unstructured":"Olid, D., F\u00e1cil, J.M., Civera, J.: Single-view place recognition under seasonal changes. arXiv preprint arXiv:1808.06516 (2018)"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Pepperell, E., Corke, P.I., Milford, M.J.: All-environment visual place recognition with smart. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1612\u20131618 (2014)","DOI":"10.1109\/ICRA.2014.6907067"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision (ICCV), pp. 2564\u20132571 (2011)","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"8_CR31","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)"},{"key":"8_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations, pp. 1\u201314 (2015)"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., Milford, M.: On the performance of convnet features for place recognition. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4297\u20134304 (2015)","DOI":"10.1109\/IROS.2015.7353986"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., et al.: Place recognition with convnet landmarks: viewpoint-robust, condition-robust, training-free. Robot. Sci. Syst. (RSS) XI, 1\u201310 (2015)","DOI":"10.15607\/RSS.2015.XI.022"},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Tomit\u0103, M.A., Zaffar, M., Milford, M., McDonald-Maier, K., Ehsan, S.: Convsequential-slam: a sequence-based, training-less visual place recognition technique for changing environments. arXiv preprint arXiv:2009.13454 (2020)","DOI":"10.1109\/ACCESS.2021.3107778"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Xiang, R., Liu, Y., Zhang, Q., Cheng, J.: Spatial pyramid pooling based convolutional autoencoder network for loop closure detection. In: IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 714\u2013719 (2019)","DOI":"10.1109\/RCAR47638.2019.9044155"},{"issue":"2","key":"8_CR37","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1109\/LRA.2020.2969917","volume":"5","author":"M Zaffar","year":"2020","unstructured":"Zaffar, M., Ehsan, S., Milford, M., McDonald-Maier, K.: CoHOG: a light-weight, compute-efficient, and training-free visual place recognition technique for changing environments. IEEE Robot. Autom. Lett. 5(2), 1835\u20131842 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"8_CR38","unstructured":"Zaffar, M., Khaliq, A., Ehsan, S., Milford, M., Alexis, K., McDonald-Maier, K.: Are state-of-the-art visual place recognition techniques any good for aerial robotics? arXiv preprint arXiv:1904.07967 (2019)"},{"issue":"6","key":"8_CR39","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 40(6), 1452\u20131464 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88007-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T13:10:25Z","timestamp":1725973825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88007-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880064","9783030880071"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88007-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/2021\/index_en.html","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"513","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":"201","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":"39% - 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":"5","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":"There were 30 oral and 171 poster presentations at the conference.","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)"}}]}}