{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:16:27Z","timestamp":1760242587559,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,10,31]],"date-time":"2017-10-31T00:00:00Z","timestamp":1509408000000},"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>This work deals with the development of a node-based monocular visual methodology for autonomous vehicle navigation which has the goal of exploring unknown regions of the sea bottom with a posterior safe revisiting of them. The work accentuates characteristics of the seabed like self-similarity and backscattering. In a stepwise fashion, a visual guidance system constructs a shape similar to a narrow corridor by optimally creating a heading function on the basis of keypoints threads, which ensures future revisits. The corridor is composed of nodes and paths in between. Each path is composed of a visual-odometry-based trail which is generated in feature-poor environments, in combination with a feature-based trail which emerges in feature-rich regions. A probabilistic analysis of the uncertainties and their impact in the success rate on loop closings is carried out. We work out two case studies, the first employing an ad-hoc benchmark and the second a series of experiments in the real world. From here qualitative conclusions can be drawn out that enable us to anticipate potential applications of the approach in the field of autonomous navigation underwater.<\/jats:p>","DOI":"10.3390\/robotics6040029","type":"journal-article","created":{"date-parts":[[2017,10,31]],"date-time":"2017-10-31T12:48:31Z","timestamp":1509454111000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Node-Based Method for SLAM Navigation in Self-Similar Underwater Environments: A Case Study"],"prefix":"10.3390","volume":"6","author":[{"given":"Emanuel","family":"Trabes","sequence":"first","affiliation":[{"name":"Instituto Argentino de Oceanograf\u00eda\u2014Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (IADO-CONICET), Florida 8000, Bah\u00eda Blanca, Argentina"},{"name":"Departamento de Ingenier\u00eda El\u00e9ctrica y de Computadoras\u2014(DIEC-UNS), Avenida Alem 1253, Bah\u00eda Blanca, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Jordan","sequence":"additional","affiliation":[{"name":"Instituto Argentino de Oceanograf\u00eda\u2014Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (IADO-CONICET), Florida 8000, Bah\u00eda Blanca, Argentina"},{"name":"Departamento de Ingenier\u00eda El\u00e9ctrica y de Computadoras\u2014(DIEC-UNS), Avenida Alem 1253, Bah\u00eda Blanca, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,31]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, N., Reid, I.D., and Leonard, J.J. (arXiv, 2016). Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age, arXiv.","DOI":"10.1109\/TRO.2016.2624754"},{"key":"ref_3","unstructured":"Inzartsev, A.V. (2009). Computer Vision Applications in the Navigation of Unmanned Underwater Vehicles. Robotics, Mobile Robotics, \u201cUnderwater Vehicles\u201d, InTech. Chapter 11."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3182\/20120213-3-IN-4034.00020","article-title":"Monocular Vision Based Navigation and Localisation in Indoor Environments","volume":"45","author":"Agarwal","year":"2012","journal-title":"IFAC Proc. Vol."},{"key":"ref_5","unstructured":"Lindsey, G.R. (2008, March 03). The Submarine Environment. Available online: http:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00396336408440456."},{"key":"ref_6","unstructured":"Trabes, E., and Jordan, M.A. (2015, January 19\u201322). On-line Filtering of Sunlight Caustic Waves in Underwater Scenes in Motion. Proceedings of the 7th International Scientific Conference on Physcon, Istanbul, Turkey."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Trabes, E., and Jordan, M.A. (2015, January 6\u20139). Self-Tuning of a Sunlight-Deflickering Filter for Moving Scenes Underwater. Proceedings of the 2015 XVI Workshop on Information Processing and Control (RPIC), C\u00f3rdoba, Argentina.","DOI":"10.1109\/RPIC.2015.7497107"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shechtman, E., and Irani, M. (2007, January 17\u201322). Matching Local Self-Similarities across Images and Videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383198"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Goedem\u00e9, T., Tuytelaars, T., and Van Gool, L. (2008). Visual Topological Map Building in Self-similar Environments. Informatics in Control Automation and Robotics, Springer.","DOI":"10.1007\/978-3-540-79142-3_16"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1002\/rob.20342","article-title":"Visual Teach and Repeat for Long-Range Rover Autonomy","volume":"27","author":"Furgale","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chaves, S.M., and Eustice, R.E. (2016, January 9\u201314). Efficient planning with the Bayes tree for active SLAM. Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2016 IEEE\/RSJ, Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759686"},{"key":"ref_12","first-page":"1","article-title":"Path Planning for Active SLAM Based on the D* Algorithm with Negative Edge Weights","volume":"Volume PP","author":"Maurovi","year":"2017","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mu, B., Giamou, M., Paull, L., Agha-mohammadi, A.A., Leonard, J., and How, J. (2016, January 12\u201314). Information-based Active SLAM via topological feature graphs. Proceedings of the IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, USA.","DOI":"10.1109\/CDC.2016.7799127"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Clarkson, K.L. (1983, January 7\u20139). Fast Algorithms for the All Nearest Neighbors Problem. 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