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Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment

2024Conference paper Open access
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dc.contributor.authorNowakowski, Marek
dc.contributor.authorKurylo, Jakub
dc.contributor.authorBraun, João
dc.contributor.authorBerger, Guido
dc.contributor.authorMendes, João
dc.contributor.authorLima, José
dc.date.accessioned2024-10-07T13:28:51Z
dc.date.available2024-10-07T13:28:51Z
dc.date.issued2024
dc.description.abstractNowadays, there has been a growing interest in the use of mobile robots for various applications,where the analysis of the operational environment is a crucial component to conduct our special tasks ormissions. Themain aimof thiswork was to implement artificial intelligence (AI) for object detection and distance estimation navigating the developed unmanned platform in unknown environments. Conventional approaches are based on vision systems analysis using neural networks for object detection, classification, and distance estimation. Unfortunately, in the case of precise operation, the used algorithms do not provide accurate data required by platforms operators as well as autonomy subsystems. To overcome this limitation, the authors propose a novel approach using the spatial data from laser scanners supplementing the acquisition of precise information about the detected object distance in the operational environment. In this article, we introduced the application of pretrained neural network models, typically used for vision systems, in analysing flat distributions of LiDAR point cloud surfaces. To achieve our goal, we have developed software that fuses detection algorithm(based on YOLO network) to detect objects and estimate their distances using theMiDaS depth model. Initially, the accuracy of distance estimationwas evaluated through video streamtesting in various scenarios. Furthermore, we have incorporated data from a laser scanner into the software, enabling precise distance measurements of the detected objects. The paper provides discussion on conducted experiments, obtained results, and implementation to improve performance of the described modular mobile platform.pt_PT
dc.description.sponsorshipThis work was supported under research work no. 55.23615.PR and 55.2022489.PL at the Military Institute of Armoured and Automotive Technology.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationNowakowski, Marek; Kurylo, Jakub; Braun, João; Berger, Guido S.; Mendes, João; Lima, José (2024). Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 118–131. ISBN 978-3-031-53035-7.pt_PT
dc.identifier.doi10.1007/978-3-031-53036-4_9pt_PT
dc.identifier.isbn978-3-031-53035-7
dc.identifier.isbn978-3-031-53036-4
dc.identifier.urihttp://hdl.handle.net/10198/30324
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectConvolutional Neural Networkpt_PT
dc.subjectDepth Estimationpt_PT
dc.subjectPoint Cloudspt_PT
dc.titleUsing LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environmentpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage131pt_PT
oaire.citation.startPage118pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
person.familyNameBraun
person.familyNameBerger
person.familyNameMendes
person.familyNameLima
person.givenNameJoão A.
person.givenNameGuido
person.givenNameJoão
person.givenNameJosé
person.identifier2726655
person.identifierR-000-8GD
person.identifier.ciencia-idBF13-D66B-7D08
person.identifier.ciencia-id931A-887B-D75D
person.identifier.ciencia-idEA1F-844D-6BA9
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0003-0276-4314
person.identifier.orcid0000-0002-4100-1494
person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id57211244317
person.identifier.scopus-author-id57225794972
person.identifier.scopus-author-id55851941311
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication.latestForDiscoveryb5c9de22-cf9e-47b8-b7a4-26e08fb12b28

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