{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:20:25Z","timestamp":1768281625228,"version":"3.49.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum-Cent Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Building up a map is essential for mobile robots to localize their position and perfect autonomous navigation which is known as Simultaneous Localization and Mapping (SLAM). The map has become very important when the weather is inappropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors in its detection accuracy. The robot had difficulty identifying its previously visited path, which is called loop-closure detection when the climate changed immensely e.g. seasonal changes. The main goal of this work is to apply Independent Component Analysis (ICA) and Auto-Encoder (Convolutional Auto-Encoder and Fundamental Auto-Encoder) to understand the route through the robot. During the operation of robots across a wide range of environmental changing conditions, the ICA has auspicious potential to extract descriptors of condition-invariant images. On the other hand, Auto-Encoder has the capability to differentiate condition variant and condition invariant characteristics of a site and identify the most possible route for the robot. In order to complete this work perfectly, we used three seasonal datasets, they are Summer\u2013Fall, Spring\u2013Fall, and Summer\u2013Spring datasets. This work uses the baseline method with a precision-recall curve and evaluates the performance of our proposed algorithm, especially the ICA algorithm. In short, the proposed algorithm ICA showed a 91.05% accuracy rate which is better than the baseline algorithm.<\/jats:p>","DOI":"10.1007\/s44230-022-00013-z","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T12:06:27Z","timestamp":1670241987000},"page":"13-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition"],"prefix":"10.1007","volume":"3","author":[{"given":"Md. Tariqul","family":"Islam","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6504-4192","authenticated-orcid":false,"given":"Khan Md.","family":"Hasib","sequence":"additional","affiliation":[]},{"given":"Md. Mahbubur","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Abdur Nur","family":"Tusher","sequence":"additional","affiliation":[]},{"given":"Mohammad Shafiul","family":"Alam","sequence":"additional","affiliation":[]},{"given":"Md. Rafiqul","family":"Islam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/A:1007919917506","volume":"25","author":"R Basri","year":"1997","unstructured":"Basri R, Jacobs DW. Recognition using region correspondences. Int J Comput Vision. 1997;25:145\u201366.","journal-title":"Int J Comput Vision"},{"key":"13_CR2","first-page":"118","volume":"8","author":"B Bellekens","year":"2015","unstructured":"Bellekens B, Spruyt V, Berkvens R, Penne R, Weyn M. A benchmark survey of rigid 3d point cloud registration algorithms. Int J Adv Intell Syst. 2015;8:118\u201327.","journal-title":"Int J Adv Intell Syst"},{"key":"13_CR3","first-page":"12","volume":"91","author":"L Bottou","year":"1991","unstructured":"Bottou L, et al. Stochastic gradient learning in neural networks. Proc Neuro-N\u0131mes. 1991;91:12.","journal-title":"Proc Neuro-N\u0131mes"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Chen Z, Jacobson A, Sunderhauf N, Upcroft B, Liu L, Shen C, Reid I, Milford M. Deep learning features at scale for visual place recognition. In: 2017 IEEE international conference on robotics and automation (ICRA). IEEE; 2017. p. 3223\u20133230.","DOI":"10.1109\/ICRA.2017.7989366"},{"key":"13_CR5","unstructured":"Chingovska I, Anjos A, Marcel S. On the effectiveness of local binary patterns in face anti-spoofing. In: 2012 BIOSIG-proceedings of the international conference of biometrics special interest group (BIOSIG). IEEE; 2012. p. 1\u20137."},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Deb S, Islam SMR, Robaiat Mou J, Islam MT. Design and implementation of low cost ECG monitoring system for the patient using smart device. In: 2017 International conference on electrical, computer and communication engineering (ECCE). IEEE; 2017, February. p. 774\u2013778.","DOI":"10.1109\/ECACE.2017.7913007"},{"key":"13_CR7","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/TPAMI.1987.4767935","volume":"9","author":"WEL Grimson","year":"1987","unstructured":"Grimson WEL, Lozano-Perez T. Localizing overlapping parts by searching the interpretation tree. IEEE Trans Pattern Anal Mach Intell. 1987;9:469\u201382.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Hasib KM, Habib MA, Towhid NA, Showrov MIH. A novel deep learning based sentiment analysis of twitter data for US airline service. In: 2021 international conference on information and communication technology for sustainable development (ICICT4SD). IEEE; 2021. p. 450\u2013455","DOI":"10.1109\/ICICT4SD50815.2021.9396879"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Hasib KM, Iqbal M, Shah FM, Mahmud JA, Popel MH, Showrov M, Hossain I, Ahmed S, Rahman O. A survey of methods for managing the classification and solution of data imbalance problem. 2020. arXiv preprint arXiv:2012.11870.","DOI":"10.3844\/jcssp.2020.1546.1557"},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"108545","DOI":"10.1109\/ACCESS.2022.3213818","volume":"10","author":"KM Hasib","year":"2022","unstructured":"Hasib KM, Tanzim A, Shin J, Faruk KO, Mahmud JA, Mridha MF. BMNet-5: a novel approach of neural network to classify the genre of Bengali music based on audio features. IEEE Access. 2022;10:108545\u201363. https:\/\/doi.org\/10.1109\/ACCESS.2022.3213818.","journal-title":"IEEE Access"},{"issue":"4","key":"13_CR11","first-page":"1","volume":"11","author":"KM Hasib","year":"2021","unstructured":"Hasib KM, Towhid NA, Islam MR. Hsdlm: a hybrid sampling with deep learning method for imbalanced data classification. Int J Cloud Appl Comput (IJCAC). 2021;11(4):1\u201313.","journal-title":"Int J Cloud Appl Comput (IJCAC)"},{"issue":"4","key":"13_CR12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.5815\/ijigsp.2021.04.02","volume":"13","author":"MT Islam","year":"2021","unstructured":"Islam MT, Islam SMR. A new image quality index and it\u2019s application on MRI image. Int J Image Graph Signal Process (IJIGSP). 2021;13(4):14\u201332.","journal-title":"Int J Image Graph Signal Process (IJIGSP)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Islam SMR, Islam MT, Huang X. A new approach of image quality index. In: 2017 4th international conference on advances in electrical engineering (ICAEE). IEEE; 2017.","DOI":"10.1109\/ICAEE.2017.8255357"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Islam MR, Razzak I, Wang X, Tilocca P, Xu G. Ucbvis: understanding customer behavior sequences with visual interactive system. In: 2021 international joint conference on neural networks (IJCNN). IEEE; 2021. p. 1\u20138.","DOI":"10.1109\/IJCNN52387.2021.9533354"},{"key":"13_CR15","unstructured":"Ivakhnenko A. Cybernetic predicting devices. Technical report."},{"key":"13_CR16","first-page":"1106","volume":"5","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;5:1106\u201314.","journal-title":"Adv Neural Inf Process Syst"},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436\u201344.","journal-title":"Nature"},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/0004-3702(87)90070-1","volume":"31","author":"DG Lowe","year":"1987","unstructured":"Lowe DG. Three-dimensional object recognition from single twodimensional images. Artif Intell. 1987;31:355\u201395.","journal-title":"Artif Intell"},{"key":"13_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-6524-8","volume-title":"Handbook of biometric anti-spoofing","author":"S Marcel","year":"2014","unstructured":"Marcel S, Nixon MS, Li SZ. Handbook of biometric anti-spoofing, vol. 1. Berlin: Springer; 2014."},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"395","DOI":"10.3844\/jcssp.2019.395.415","volume":"15","author":"H Md","year":"2019","unstructured":"Md H, Islam R, Haque MA, Hossain MS, Ul-Haq A, Sawan JJ. Automated face detection, recognition and gender estimation applied to person identification. J Comput Sci. 2019;15:395\u2013415.","journal-title":"J Comput Sci"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Noh H, Araujo A, Sim J, Weyand T, Han B. Large-scale image retrieval with attentive deep local features. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 3456\u20133465.","DOI":"10.1109\/ICCV.2017.374"},{"key":"13_CR22","first-page":"1","volume":"5","author":"D Rumelhart","year":"1988","unstructured":"Rumelhart D, Hinton G, Williams R. Learning representations by backpropagating errors, cognitive model. Cogn Model. 1988;5:1.","journal-title":"Cogn Model"},{"key":"13_CR23","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/34.589215","volume":"19","author":"C Schmid","year":"1997","unstructured":"Schmid C, Mohr R. Local grayvalue invariants for image retrieval. IEEE Trans Pattern Anal Mach Intell. 1997;19:530\u20135.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Wang TH, Huang HJ, Lin JT, Hu CW, Zeng KH, Sun M. Omnidirectional cnn for visual place recognition and navigation. In: 2018 IEEE international conference on robotics and automation (ICRA). IEEE; 2018. p. 2341\u20132348.","DOI":"10.1109\/ICRA.2018.8463173"},{"key":"13_CR25","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/978-1-59745-198-7_2","volume-title":"The protein protocols handbook","author":"JH Waterborg","year":"2009","unstructured":"Waterborg JH. The lowry method for protein quantitation. In: The protein protocols handbook. Berlin: Springer; 2009. p. 7\u201310."},{"key":"13_CR26","unstructured":"Zarini H, Khalili A, Tabassum H, Rasti M, Saad W. AlexNet classifier and support vector regressor for scheduling and power control in multimedia heterogeneous networks. In: IEEE Transactions on Mobile Computing, 2021."},{"key":"13_CR27","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/0004-3702(95)00022-4","volume":"78","author":"Z Zhang","year":"1995","unstructured":"Zhang Z, Deriche R, Faugeras O, Luong QT. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artif Intell. 1995;78:87\u2013119.","journal-title":"Artif Intell"}],"container-title":["Human-Centric Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-022-00013-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44230-022-00013-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-022-00013-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T19:14:17Z","timestamp":1679685257000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44230-022-00013-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["13"],"URL":"https:\/\/doi.org\/10.1007\/s44230-022-00013-z","relation":{},"ISSN":["2667-1336"],"issn-type":[{"value":"2667-1336","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]},"assertion":[{"value":"21 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we do not have any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}