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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28384
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dc.contributor.authorFathi, Kiavash-
dc.contributor.authorDarvishy, Alireza-
dc.contributor.authorvan de Venn, Hans Wernher-
dc.date.accessioned2023-08-04T08:48:12Z-
dc.date.available2023-08-04T08:48:12Z-
dc.date.issued2023-
dc.identifier.isbn979-8-3503-4647-3de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28384-
dc.description.abstractLoop closure is an inseparable part of any accurate and reliable visual simultaneous localization and mapping (SLAM) algorithm for autonomous vehicles and mobile robots. Loop closure potentially decreases the impact of the cumulative drift while generating the map of the traversed environment. In this paper, a heuristic similarity and location-based approach for loop closure in unexplored environments is introduced. The current SLAM implementation on average requires 0.295 seconds per frame from which only 0.0270 seconds are the runtime latencies of the similarity and location-based real-time loop closure (SNAPS), which includes trajectory correction. The proposed approach results in a 65% decrease in the mean deviation from the ground truth. In the conducted study, neither conventional bag-of-words models, nor computationally expensive deep neural networks have been used to detect and perform loop closure, which makes the proposed approach both interpretable and efficient. In fact, we propose a method which tries to find loop closure candidates based on the location and also an interpretable similarity score attained from the generated thumbnails of the read frames instead of the local descriptors. Additionally, the employed discount factor applied on the pose trajectory update rule guarantees a consistent and accurate map. Lastly, the KITTI dataset is used to demonstrate the efficiency and accuracy of SNAPS for SLAM.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectVisual simultaneous localization and mappingde_CH
dc.subjectLocalizationde_CH
dc.subjectVisual odometryde_CH
dc.subjectAutonomous vehiclesde_CH
dc.subjectLoop closure detectionde_CH
dc.subjectReal-time systemde_CH
dc.subjectComputational modelingde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleSimilarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environmentsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Mechatronische Systeme (IMS)de_CH
dc.identifier.doi10.1109/COINS57856.2023.10189300de_CH
dc.identifier.doi10.21256/zhaw-28384-
zhaw.conference.detailsIEEE International Conference on omni-layer intelligent Systems (COINS), Berlin, Germany and online, 23-35 July 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start7de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS)de_CH
zhaw.webfeedHuman-Centered Computingde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Fathi, K., Darvishy, A., & van de Venn, H. W. (2023). Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments [Conference paper]. 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 7. https://doi.org/10.1109/COINS57856.2023.10189300
Fathi, K., Darvishy, A. and van de Venn, H.W. (2023) ‘Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments’, in 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, p. 7. Available at: https://doi.org/10.1109/COINS57856.2023.10189300.
K. Fathi, A. Darvishy, and H. W. van de Venn, “Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments,” in 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 2023, p. 7. doi: 10.1109/COINS57856.2023.10189300.
FATHI, Kiavash, Alireza DARVISHY und Hans Wernher VAN DE VENN, 2023. Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments. In: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS). Conference paper. IEEE. 2023. S. 7. ISBN 979-8-3503-4647-3
Fathi, Kiavash, Alireza Darvishy, and Hans Wernher van de Venn. 2023. “Similarity and Location-Based Real-Time Loop Closure : SNAPS for SLAM in Unexplored-Environments.” Conference paper. In 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 7. IEEE. https://doi.org/10.1109/COINS57856.2023.10189300.
Fathi, Kiavash, et al. “Similarity and Location-Based Real-Time Loop Closure : SNAPS for SLAM in Unexplored-Environments.” 2023 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), IEEE, 2023, p. 7, https://doi.org/10.1109/COINS57856.2023.10189300.


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