{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T03:22:34Z","timestamp":1772248954245,"version":"3.50.1"},"reference-count":78,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Visual place recognition (VPR) is the ability to recognize locations in a physical environment based only on visual inputs. It is a challenging task due to perceptual aliasing, viewpoint and appearance variations and complexity of dynamic scenes. Despite promising demonstrations, many state-of-the-art (SOTA) VPR approaches based on artificial neural networks (ANNs) suffer from computational inefficiency. However, spiking neural networks (SNNs) implemented on neuromorphic hardware are reported to have remarkable potential for more efficient solutions computationally. Still, training SOTA SNNs for VPR is often intractable on large and diverse datasets, and they typically demonstrate poor real-time operation performance. To address these shortcomings, we developed an end-to-end convolutional SNN model for VPR that leverages backpropagation for tractable training. Rate-based approximations of leaky integrate-and-fire (LIF) neurons are employed during training, which are then replaced with spiking LIF neurons during inference. The proposed method significantly outperforms existing SOTA SNNs on challenging datasets like Nordland and Oxford RobotCar, achieving 78.6% precision at 100% recall on the Nordland dataset (compared to 73.0% from the current SOTA) and 45.7% on the Oxford RobotCar dataset (compared to 20.2% from the current SOTA). Our approach offers a simpler training pipeline while yielding significant improvements in both training and inference times compared to SOTA SNNs for VPR. Hardware-in-the-loop tests using Intel's neuromorphic USB form factor, Kapoho Bay, show that our on-chip spiking models for VPR trained via the ANN-to-SNN conversion strategy continue to outperform their SNN counterparts, despite a slight but noticeable decrease in performance when transitioning from off-chip to on-chip, while offering significant energy efficiency. The results highlight the outstanding rapid prototyping and real-world deployment capabilities of this approach, showing it to be a substantial step toward more prevalent SNN-based real-world robotics solutions.<\/jats:p>","DOI":"10.3389\/fnbot.2024.1490267","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T01:55:35Z","timestamp":1738115735000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition"],"prefix":"10.3389","volume":"18","author":[{"given":"Ugur","family":"Akcal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan Georgiev","family":"Raikov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ekaterina Dmitrievna","family":"Gribkova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anwesa","family":"Choudhuri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung Hyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattia","family":"Gazzola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rhanor","family":"Gillette","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Soltesz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Girish","family":"Chowdhary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.neucom.2022.09.127","article-title":"GSV-cities: toward appropriate supervised visual place recognition","volume":"513","author":"Ali-bey","year":"2022","journal-title":"Neurocomputing"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00301","article-title":"\u201cMixvpr: feature mixing for visual place recognition,\u201d","author":"Ali-Bey","year":"2023","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.572","article-title":"\u201cNetvlad: CNN architecture for weakly supervised place recognition,\u201d","author":"Arandjelovic","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B4","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1109\/CVPR.2012.6248018","article-title":"\u201cThree things everyone should know to improve object retrieval,\u201d","volume-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition","author":"Arandjelovi\u0107","year":"2012"},{"key":"B5","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","article-title":"\u201cSURF: speeded up robust features,\u201d","author":"Bay","year":"2006","journal-title":"Computer Vision"},{"key":"B6","unstructured":"Belgaid\n              M. C.\n            \n            \n              Rouvoy\n              R.\n            \n            \n              Seinturier\n              L.\n            \n          \n          pyJoules: Python library that measures python code snippets\n          \n          2019"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00483","article-title":"\u201cRethinking visual geo-localization for large-scale applications,\u201d","author":"Berton","year":"2022","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01017","article-title":"\u201cEigenplaces: training viewpoint robust models for visual place recognition,\u201d","author":"Berton","year":"2023","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00422-006-0068-6","article-title":"A review of the integrate-and-fire neuron model: I. homogeneous synaptic input","volume":"95","author":"Burkitt","year":"2006","journal-title":"Biol. Cyber"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1109\/TPAMI.2011.222","article-title":"Brief: computing a local binary descriptor very fast","volume":"34","author":"Calonder","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"B11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ECMR.2019.8870948","article-title":"\u201cSpatio-semantic convnet-based visual place recognition,\u201d","volume-title":"2019 European Conference on Mobile Robots (ECMR)","author":"Camara","year":"2019"},{"key":"B12","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1007\/978-3-030-58565-5_43","article-title":"\u201cUnifying deep local and global features for image search,\u201d","volume-title":"Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XX 16","author":"Cao","year":"2020"},{"key":"B13","first-page":"24975","article-title":"\u201cDifferentiable hierarchical and surrogate gradient search for spiking neural networks,\u201d","author":"Che","year":"2022","journal-title":"36th Conference on Neural Information Processing Systems (NeurIPS 2022)"},{"key":"B14","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1109\/ICRA.2017.7989366","article-title":"\u201cDeep learning features at scale for visual place recognition,\u201d","volume-title":"2017 IEEE International Conference on Robotics and Automation (ICRA)","author":"Chen","year":"2017"},{"key":"B15","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MRS.2017.8250934","article-title":"\u201cEfficient decentralized visual place recognition from full-image descriptors,\u201d","volume-title":"2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)","author":"Cieslewski","year":"2017"},{"key":"B16","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/JPROC.2021.3067593","article-title":"Advancing neuromorphic computing with loihi: a survey of results and outlook","volume":"109","author":"Davies","year":"2021","journal-title":"Proc. IEEE"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00060","article-title":"\u201cSuperpoint: self-supervised interest point detection and description,\u201d","author":"DeTone","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops"},{"key":"B18","doi-asserted-by":"publisher","first-page":"568359","DOI":"10.3389\/fnbot.2020.568359","article-title":"Nengo and low-power ai hardware for robust, embedded neurorobotics","volume":"14","author":"DeWolf","year":"2020","journal-title":"Front. Neurorobot"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00941","article-title":"\u201cScalable place recognition under appearance change for autonomous driving,\u201d","author":"Doan","year":"2019","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"B20","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1177\/0278364919863090","article-title":"Segmap: segment-based mapping and localization using data-driven descriptors","volume":"39","author":"Dube","year":"2020","journal-title":"Int. J. Rob. Res"},{"key":"B21","first-page":"4416","article-title":"\u201cWhere is your place, visual place recognition?\u201d","volume-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21)","author":"Garg","year":"2021"},{"key":"B22","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1177\/0278364919839761","article-title":"Semantic-geometric visual place recognition: a new perspective for reconciling opposing views","volume":"41","author":"Garg","year":"2022","journal-title":"Int. J. Rob. Res"},{"key":"B23","doi-asserted-by":"crossref","first-page":"4195","DOI":"10.1109\/ICRA40945.2020.9197133","article-title":"\u201cEvent-based angular velocity regression with spiking networks,\u201d","volume-title":"2020 IEEE International Conference on Robotics and Automation (ICRA)","author":"Gehrig","year":"2020"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1234962","DOI":"10.3389\/fnbot.2023.1234962","article-title":"Autonomous driving controllers with neuromorphic spiking neural networks","volume":"17","author":"Halaly","year":"2023","journal-title":"Front. Neurorobot"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01392","article-title":"\u201cPatch-netvlad: multi-scale fusion of locally-global descriptors for place recognition,\u201d","author":"Hausler","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B26","doi-asserted-by":"publisher","first-page":"4528","DOI":"10.3390\/app12094528","article-title":"Neuromorphic neural engineering framework-inspired online continuous learning with analog circuitry","volume":"12","author":"Hazan","year":"2022","journal-title":"Appl. Sci"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00069","article-title":"\u201cLocal descriptors optimized for average precision,\u201d","author":"He","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B28","doi-asserted-by":"crossref","first-page":"10200","DOI":"10.1109\/ICRA57147.2024.10610918","article-title":"\u201cVprtempo: a fast temporally encoded spiking neural network for visual place recognition,\u201d","volume-title":"2024 IEEE International Conference on Robotics and Automation (ICRA)","author":"Hines","year":"2024"},{"key":"B29","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1109\/TNNLS.2021.3119238","article-title":"Spiking deep residual networks","volume":"34","author":"Hu","year":"2021","journal-title":"IEEE Trans. Neur. Netw. Learn. Syst"},{"key":"B30","article-title":"Spiking deep networks with lif neurons","author":"Hunsberger","year":"2015","journal-title":"arXiv preprint arXiv:1510.08829"},{"key":"B31","doi-asserted-by":"publisher","first-page":"4094","DOI":"10.1109\/LRA.2022.3149030","article-title":"Spiking neural networks for visual place recognition via weighted neuronal assignments","volume":"7","author":"Hussaini","year":"2022","journal-title":"IEEE Robot. Autom. Lett"},{"key":"B32","doi-asserted-by":"crossref","first-page":"4200","DOI":"10.1109\/ICRA48891.2023.10160749","article-title":"\u201cEnsembles of compact, region-specific &regularized spiking neural networks for scalable place recognition,\u201d","volume-title":"2023 IEEE International Conference on Robotics and Automation (ICRA)","author":"Hussaini","year":"2023"},{"key":"B33","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/ICCV.2011.6126328","article-title":"\u201cFrom images to scenes: compressing an image cluster into a single scene model for place recognition,\u201d","volume-title":"2011 International Conference on Computer Vision","author":"Johns","year":"2011"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.139","article-title":"\u201cPredicting good features for image geo-localization using per-bundle vlad,\u201d","author":"Kim","year":"2015","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6787","article-title":"\u201cSpiking-yolo: spiking neural network for energy-efficient object detection,\u201d","author":"Kim","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"B36","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"B37","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1109\/LRA.2022.3232033","article-title":"Self-supervised domain calibration and uncertainty estimation for place recognition","volume":"8","author":"Lajoie","year":"2022","journal-title":"IEEE Robot. Autom. Lett"},{"key":"B38","volume-title":"Learn ARCore-Fundamentals of Google ARCore: Learn to build augmented reality apps for Android, Unity, and the web with Google ARCore 1.0","author":"Lanham","year":"2018"},{"key":"B39","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TMC.2018.2842751","article-title":"Vision-based mobile indoor assistive navigation aid for blind people","volume":"18","author":"Li","year":"2018","journal-title":"IEEE Trans. Mobile Comput"},{"key":"B40","first-page":"1051","article-title":"\u201cVisual loop closure detection with a compact image descriptor,\u201d","volume-title":"2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems","author":"Liu","year":"2012"},{"key":"B41","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.patcog.2016.05.021","article-title":"Object recognition using local invariant features for robotic applications: a survey","volume":"60","author":"Loncomilla","year":"2016","journal-title":"Pattern Recognit"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1109\/ICCV.1999.790410","author":"Lowe","year":"1999"},{"key":"B43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TRO.2015.2496823","article-title":"Visual place recognition: a survey","volume":"32","author":"Lowry","year":"2015","journal-title":"IEEE Trans. Robot"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2015.XI.037","article-title":"\u201cGet out of my lab: large-scale, real-time visual-inertial localization,\u201d","author":"Lynen","year":"2015","journal-title":"Robotics: Science and Systems"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1177\/0278364920931151","article-title":"Large-scale, real-time visual-inertial localization revisited","volume":"39","author":"Lynen","year":"2020","journal-title":"Int. J. Rob. Res"},{"key":"B46","article-title":"Real-time kinematic ground truth for the oxford robotcar dataset","author":"Maddern","year":"2020","journal-title":"arXiv preprint arXiv:2002.10152"},{"key":"B47","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0278364916679498","article-title":"\u201c1 Year, 1000km: the Oxford RobotCar dataset","volume":"36","author":"Maddern","year":"2017","journal-title":"Int. J. Robot. Res"},{"key":"B48","doi-asserted-by":"publisher","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":"B49","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust wide-baseline stereo from maximally stable extremal regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput"},{"key":"B50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15607\/RSS.2014.X.023","article-title":"Scene signatures: localised and point-less features for localisation","volume":"10","author":"McManus","year":"2014","journal-title":"Robotics"},{"key":"B51","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/3-540-47969-4_9","article-title":"\u201cAn affine invariant interest point detector,\u201d","volume-title":"Computer Vision-ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28\u201331, 2002 Proceedings, Part I 7","author":"Mikolajczyk","year":"2002"},{"key":"B52","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/TRO.2017.2788045","article-title":"Robust visual localization across seasons","volume":"34","author":"Naseer","year":"2018","journal-title":"IEEE Trans. Robot"},{"key":"B53","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","article-title":"Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks","volume":"36","author":"Neftci","year":"2019","journal-title":"IEEE Signal Proc. Mag"},{"key":"B54","article-title":"\u201cSingle-view place recognition under seasonal changes,\u201d","author":"Olid","year":"2018","journal-title":"PPNIV Workshop at IROS 2018"},{"key":"B55","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/S0079-6123(06)55002-2","article-title":"Building the gist of a scene: the role of global image features in recognition","volume":"155","author":"Oliva","year":"2006","journal-title":"Prog. Brain Res"},{"key":"B56","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.neunet.2019.08.009","article-title":"Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari breakout game","volume":"120","author":"Patel","year":"2019","journal-title":"Neural Netw"},{"key":"B57","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1109\/CVPR.2010.5540009","article-title":"\u201cLarge-scale image retrieval with compressed fisher vectors,\u201d","volume-title":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"Perronnin","year":"2010"},{"key":"B58","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","article-title":"Fine-tuning CNN image retrieval with no human annotation","volume":"41","author":"Radenovi\u0107","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"B59","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s12021-019-09424-z","article-title":"NengoDL: Combining deep learning and neuromorphic modelling methods","volume":"17","author":"Rasmussen","year":"2018","journal-title":"Neuroinformatics"},{"key":"B60","unstructured":"Reinhardt\n              T.\n            \n          \n          Using Global Localization to Improve Navigation\n          \n          2019"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00521","article-title":"\u201cLearning with average precision: training image retrieval with a listwise loss,\u201d","author":"Revaud","year":"2019","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"B62","doi-asserted-by":"publisher","first-page":"682","DOI":"10.3389\/fnins.2017.00682","article-title":"Conversion of continuous-valued deep networks to efficient event-driven networks for image classification","volume":"11","author":"Rueckauer","year":"2017","journal-title":"Front. Neurosci"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00721","article-title":"\u201cSemantic visual localization,\u201d","author":"Sch\u00f6nberger","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_33","article-title":"\u201cCplanet: enhancing image geolocalization by combinatorial partitioning of maps,\u201d","author":"Seo","year":"2018","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"B65","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/ROBIO.2015.7418753","article-title":"\u201cGoogle map aided visual navigation for uavs in gps-denied environment,\u201d","volume-title":"2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)","author":"Shan","year":"2015"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01192","article-title":"\u201cLocal features and visual words emerge in activations,\u201d","author":"Sim\u00e9oni","year":"2019","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B67","article-title":"Making a spiking net work: robust brain-like unsupervised machine learning","author":"Stratton","year":"2022","journal-title":"arXiv preprint arXiv:2208.01204"},{"key":"B68","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1109\/IROS.2015.7353986","article-title":"\u201cOn the performance of convnet features for place recognition,\u201d","volume-title":"2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","author":"S\u00fcnderhauf","year":"2015"},{"key":"B69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/CVPR.2008.4587633","article-title":"\u201cSmall codes and large image databases for recognition,\u201d","volume-title":"2008 IEEE Conference on Computer Vision and Pattern Recognition","author":"Torralba","year":"2008"},{"key":"B70","doi-asserted-by":"publisher","first-page":"19929","DOI":"10.1109\/TITS.2022.3175656","article-title":"The revisiting problem in simultaneous localization and mapping: a survey on visual loop closure detection","volume":"23","author":"Tsintotas","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst"},{"key":"B71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00470","article-title":"\u201cPoint-netvlad: deep point cloud based retrieval for large-scale place recognition,\u201d","author":"Uy","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B72","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/ICRA48506.2021.9560881","article-title":"\u201cEvent-driven vision and control for uavs on a neuromorphic chip,\u201d","volume-title":"2021 IEEE International Conference on Robotics and Automation (ICRA)","author":"Vitale","year":"2021"},{"key":"B73","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-3-319-46484-8_3","article-title":"\u201cPlanet-photo geolocation with convolutional neural networks,\u201d","volume-title":"Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VIII 14","author":"Weyand","year":"2016"},{"key":"B74","doi-asserted-by":"crossref","first-page":"7137","DOI":"10.1109\/ICRA.2019.8793853","article-title":"\u201cMrs-VPR: a multi-resolution sampling based global visual place recognition method,\u201d","volume-title":"2019 International Conference on Robotics and Automation (ICRA)","author":"Yin","year":"2019"},{"key":"B75","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1109\/TNNLS.2019.2908982","article-title":"Spatial pyramid-enhanced netvlad with weighted triplet loss for place recognition","volume":"31","author":"Yu","year":"2020","journal-title":"IEEE Trans. Neur. Netw. Learn. Syst"},{"key":"B76","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TPAMI.2017.2787132","article-title":"Large-scale image geo-localization using dominant sets","volume":"41","author":"Zemene","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"B77","doi-asserted-by":"publisher","first-page":"107760","DOI":"10.1016\/j.patcog.2020.107760","article-title":"Visual place recognition: a survey from deep learning perspective","volume":"113","author":"Zhang","year":"2021","journal-title":"Pattern Recognit"},{"key":"B78","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/978-3-030-64313-3_39","article-title":"\u201cSpatio-temporal memory for navigation in a mushroom body model,\u201d","volume-title":"Biomimetic and Biohybrid Systems: 9th International Conference, Living Machines 2020, Freiburg, Germany, July 28\u201330, 2020, Proceedings 9","author":"Zhu","year":"2020"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2024.1490267\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T01:55:54Z","timestamp":1738115754000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2024.1490267\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,29]]},"references-count":78,"alternative-id":["10.3389\/fnbot.2024.1490267"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2024.1490267","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.03.14.584997","asserted-by":"object"}]},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,29]]},"article-number":"1490267"}}