{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:33:56Z","timestamp":1760524436451,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T00:00:00Z","timestamp":1670112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/robotics11060142","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T01:42:21Z","timestamp":1670204541000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Place Recognition with Memorable and Stable Cues for Loop Closure of Visual SLAM Systems"],"prefix":"10.3390","volume":"11","author":[{"given":"Rafiqul","family":"Islam","sequence":"first","affiliation":[{"name":"UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9542-9525","authenticated-orcid":false,"given":"Habibullah","family":"Habibullah","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Zhang, J., Wang, X., Chen, Y., and Zhu, C. (2018). Place Recognition: An Overview of Vision Perspective. Appl. Sci., 8.","DOI":"10.3390\/app8112257"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bampis, L., Amanatiadis, A., and Gasteratos, A. (2016, January 9\u201314). Encoding the description of image sequences: A two-layered pipeline for loop closure detection. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7759667"},{"key":"ref_3","unstructured":"K\u00fcmmerle, R., Grisetti, G., Strasdat, H., Konolige, K., and Burgard, W. (2011, January 9\u201313). g2o: A general framework for graph optimization. Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1016\/j.robot.2009.06.010","article-title":"A comparison of loop closing techniques in monocular SLAM","volume":"57","author":"Williams","year":"2009","journal-title":"Robot. Auton. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1177\/0278364908090961","article-title":"FAB-MAP: Probabilistic localization and mapping in the space of appearance","volume":"27","author":"Cummins","year":"2008","journal-title":"Int. J. Robot. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Islam, R., and Habibullah, H. (2021, January 22\u201324). A Semantically Aware Place Recognition System for Loop Closure of a Visual SLAM System. Proceedings of the 2021 4th International Conference on Mechatronics, Robotics and Automation (ICMRA), Zhanjiang, China.","DOI":"10.1109\/ICMRA53481.2021.9675715"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TRO.2015.2496823","article-title":"Visual Place Recognition: A Survey","volume":"32","author":"Lowry","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Torralba, A., Murphy, K.P., Freeman, W.T., and Rubin, M.A. (2003, January 13\u201316). Context-based vision system for place and object recognition. Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238354"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/TRO.2012.2192013","article-title":"Automatic Visual Bag-of-Words for Online Robot Navigation and Mapping","volume":"28","author":"Nicosevici","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TRO.2012.2189497","article-title":"Robust Place Recognition With Stereo Sequences","volume":"28","author":"Lerma","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","unstructured":"Nist\u00e9r, D., and Stew\u00e9nius, H. (2006, January 17\u201322). Scalable Recognition with a Vocabulary Tree. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/TRO.2012.2197158","article-title":"Bags of Binary Words for Fast Place Recognition in Image Sequences","volume":"28","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TPAMI.2016.2545667","article-title":"Higher-Order Occurrence Pooling for Bags-of-Words: Visual Concept Detection","volume":"39","author":"Koniusz","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6969","DOI":"10.1109\/LRA.2021.3096751","article-title":"A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition","volume":"6","author":"Keetha","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/LRA.2022.3142741","article-title":"Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition","volume":"7","author":"Bhutta","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3882","DOI":"10.1109\/LRA.2022.3147257","article-title":"MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery","volume":"7","author":"Khaliq","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cai, K., Wang, B., and Lu, C.X. (2022, January 23\u201327). AutoPlace: Robust Place Recognition with Single-chip Automotive Radar. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811869"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cai, Y., Zhao, J., Cui, J., Zhang, F., Ye, C., and Feng, T. (2022, January 20\u201322). Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition. Proceedings of the 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Bedford, UK.","DOI":"10.1109\/MFI55806.2022.9913860"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hausler, S., Garg, S., Xu, M., Milford, M., and Fischer, T. (2021, January 20\u201325). Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01392"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12014","DOI":"10.1109\/LRA.2022.3207547","article-title":"Visual Loop Closure Detection for a Future Mars Science Helicopter","volume":"7","author":"Dietsche","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xin, Z., Cai, Y., Lu, T., Xing, X., Cai, S., Zhang, J., Yang, Y., and Wang, Y. (2019, January 20\u201324). Localizing Discriminative Visual Landmarks for Place Recognition. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794383"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., Pollefeys, M., Geiger, A., and Sattler, T. (2018, January 18\u201323). Semantic Visual Localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00721"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"19516","DOI":"10.1109\/ACCESS.2021.3054937","article-title":"A Survey on Deep Visual Place Recognition","volume":"9","author":"Masone","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Naseer, T., Oliveira, G.L., Brox, T., and Burgard, W. (June, January 29). Semantics-aware visual localization under challenging perceptual conditions. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107680","DOI":"10.1016\/j.patcog.2020.107680","article-title":"Place perception from the fusion of different image representation","volume":"110","author":"Li","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mousavian, A., Kosecka, J., and Lien, J.M. (2015, January 26\u201330). Semantically guided location recognition for outdoors scenes. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139877"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1109\/TPAMI.2017.2711011","article-title":"NetVLAD: CNN Architecture for Weakly Supervised Place Recognition","volume":"40","author":"Torii","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1049\/trit.2017.0020","article-title":"Visual navigation method for indoor mobile robot based on extended BoW model","volume":"2","author":"Li","year":"2017","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.neucom.2022.09.127","article-title":"GSV-Cities: Toward Appropriate Supervised Visual Place Recognition","volume":"513","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_32","unstructured":"S\u00fcnderhauf, N., Dayoub, F., Shirazi, S.A., Upcroft, B., and Milford, M. (October, January 28). On the performance of ConvNet features for place recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_33","unstructured":"Zhou, B., Lapedriza, \u00c0., Xiao, J., Torralba, A., and Oliva, A. (2014, January 8\u201313). Learning Deep Features for Scene Recognition using Places Database. Proceedings of the NIPS, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.1007\/s11263-021-01469-5","article-title":"VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change","volume":"129","author":"Zaffar","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","unstructured":"Jiwei, N., Feng, J.M., Xue, D., Feng, P., Wei, L., Jun, H., and Cheng, S. (2022). A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Razavian, A.S., Azizpour, H., Sullivan, J., and Carlsson, S. (2014, January 23\u201328). CNN Features Off-the-Shelf: An Astounding Baseline for Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gong, Y., Wang, L., Guo, R., and Lazebnik, S. (2014, January 6\u201312). Multi-scale orderless pooling of deep convolutional activation features. Proceedings of the 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, Y., Guo, Y., Wu, S., and Lew, M.S. (2015, January 23\u201326). Deepindex for accurate and efficient image retrieval. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, Shanghai, China.","DOI":"10.1145\/2671188.2749300"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wan, J., Wang, D., Hoi, S.C.H., Wu, P., Zhu, J., Zhang, Y., and Li, J. (2014, January 3\u20137). Deep learning for content-based image retrieval: A comprehensive study. Proceedings of the ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654948"},{"key":"ref_40","unstructured":"Gomez-Ojeda, R., Lopez-Antequera, M., Petkov, N., and Gonzalez-Jimenez, J. (2015). Training a convolutional neural network for appearance-invariant place recognition. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","article-title":"Semantic object classes in video: A high-definition ground truth database","volume":"30","author":"Brostow","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kendall, A., Badrinarayanan, V., and Cipolla, R. (2017). Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. arXiv.","DOI":"10.5244\/C.31.57"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"LoweDavid","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","article-title":"EPnP: An Accurate O(n) Solution to the PnP Problem","volume":"81","author":"Lepetit","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","unstructured":"Bonarini, A., Burgard, W., Fontana, G., Matteucci, M., Sorrenti, D.G., and Tardos, J.D. (2006, January 9\u201315). Rawseeds: Robotics advancement through web-publishing of sensorial and elaborated extensive data sets. Proceedings of the 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijing, China."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1177\/0278364909103911","article-title":"The New College Vision and Laser Data Set","volume":"28","author":"Smith","year":"2009","journal-title":"Int. J. Robot. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s10514-009-9138-7","article-title":"A collection of outdoor robotic datasets with centimeter-accuracy ground truth","volume":"27","author":"Blanco","year":"2009","journal-title":"Auton. Robot."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., and Rabinovich, A. (2020, January 13\u201319). SuperGlue: Learning Feature Matching With Graph Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., Cadena, C., Siegwart, R., and Dymczyk, M. (2019, January 15\u201320). From Coarse to Fine: Robust Hierarchical Localization at Large Scale. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01300"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/11\/6\/142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:33:47Z","timestamp":1760146427000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/11\/6\/142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,4]]},"references-count":49,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["robotics11060142"],"URL":"https:\/\/doi.org\/10.3390\/robotics11060142","relation":{},"ISSN":["2218-6581"],"issn-type":[{"type":"electronic","value":"2218-6581"}],"subject":[],"published":{"date-parts":[[2022,12,4]]}}}