{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:32:31Z","timestamp":1782844351075,"version":"3.54.5"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-25281-0","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T12:26:09Z","timestamp":1763987169000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantic orientation for indoor navigation system using large language models"],"prefix":"10.1038","volume":"15","author":[{"given":"Marzena","family":"Halama","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u0142awomir","family":"Nowak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konrad","family":"Po\u0142ys","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"25281_CR1","doi-asserted-by":"crossref","unstructured":"Vieira, M. et al. Optimizing indoor airport navigation with advanced visible light communication systems. Sensors (Basel, Switzerland) 24 (2024).","DOI":"10.3390\/s24165445"},{"key":"25281_CR2","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1177\/0264619619833723","volume":"37","author":"W Jeamwatthanachai","year":"2019","unstructured":"Jeamwatthanachai, W., Wald, M. & Wills, G. Indoor navigation by blind people: Behaviors and challenges in unfamiliar spaces and buildings. Br. J. Vis. Impairment 37, 140\u2013153 (2019).","journal-title":"Br. J. Vis. Impairment"},{"key":"25281_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10846-021-01327-z","volume":"101","author":"P Roy","year":"2021","unstructured":"Roy, P. & Chowdhury, C. A survey of machine learning techniques for indoor localization and navigation systems. J. Intell. Robot. Syst. 101, 63 (2021).","journal-title":"J. Intell. Robot. Syst."},{"key":"25281_CR4","doi-asserted-by":"publisher","first-page":"109041","DOI":"10.1016\/j.comnet.2022.109041","volume":"212","author":"SM Asaad","year":"2022","unstructured":"Asaad, S. M. & Maghdid, H. S. A comprehensive review of indoor\/outdoor localization solutions in IOT era: Research challenges and future perspectives. Comput. Netw. 212, 109041 (2022).","journal-title":"Comput. Netw."},{"key":"25281_CR5","doi-asserted-by":"crossref","unstructured":"Temeltas, H. & Kayak, D. Slam for robot navigation. In IEEE Aerospace and Electronic Systems Magazine (2008).","DOI":"10.1109\/MAES.2008.4694832"},{"key":"25281_CR6","unstructured":"Islam, Q.\u00a0U. et al. Advancing real-world visual slam: Integrating adaptive segmentation with dynamic object detection for enhanced environmental perception. Expert Syst. Appl. (2024)."},{"key":"25281_CR7","unstructured":"Islam, Q.\u00a0U., Khozaei, F., Baig, I. & Ignatyev, D. et al. Advancing autonomous slam systems: Integrating yolo object detection and enhanced loop closure techniques for robust environment mapping. Robot. Auton. Syst. (2025)."},{"key":"25281_CR8","doi-asserted-by":"publisher","first-page":"18815","DOI":"10.1038\/s41598-022-22938-y","volume":"12","author":"\u0141 Sobczak","year":"2022","unstructured":"Sobczak, \u0141, Filus, K., Doma\u0144ska, J. & Doma\u0144ski, A. Finding the best hardware configuration for 2d slam in indoor environments via simulation based on google cartographer. Sci. Rep. 12, 18815 (2022).","journal-title":"Sci. Rep."},{"key":"25281_CR9","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G.\u00a0E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)."},{"key":"25281_CR10","doi-asserted-by":"crossref","unstructured":"Ghosh, R., Mishra, A., Orchard, G. & Thakor, N.\u00a0V. Real-time object recognition and orientation estimation using an event-based camera and cnn. In 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings. 544\u2013547 (IEEE, 2014).","DOI":"10.1109\/BioCAS.2014.6981783"},{"key":"25281_CR11","doi-asserted-by":"publisher","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","volume":"82","author":"T Diwan","year":"2023","unstructured":"Diwan, T., Anirudh, G. & Tembhurne, J. V. Object detection using yolo: Challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82, 9243\u20139275 (2023).","journal-title":"Multimed. Tools Appl."},{"key":"25281_CR12","doi-asserted-by":"crossref","unstructured":"Liu, X. et al. GPT understands, too. AI Open (2024).","DOI":"10.1016\/j.aiopen.2023.08.012"},{"key":"25281_CR13","unstructured":"Wang, Z. et al. Is ChatGPT a good sentiment analyzer? A preliminary study. arXiv preprint arXiv:2304.04339 (2023)."},{"key":"25281_CR14","doi-asserted-by":"crossref","unstructured":"Kim, C.\u00a0Y., Lee, C.\u00a0P. & Mutlu, B. Understanding large-language model (llm)-powered human-robot interaction. In Proceedings of the 2024 ACM\/IEEE International Conference on Human-Robot Interaction. 371\u2013380 (2024).","DOI":"10.1145\/3610977.3634966"},{"key":"25281_CR15","doi-asserted-by":"crossref","unstructured":"Atia, M.\u00a0M., Liu, S., Nematallah, H., Karamat, T.\u00a0B. & Noureldin, A. Integrated indoor navigation system for ground vehicles with automatic 3-d alignment and position initialization. In IEEE Transactions on Vehicular Technology. 1279\u20131292 (2015).","DOI":"10.1109\/TVT.2015.2397004"},{"key":"25281_CR16","doi-asserted-by":"crossref","unstructured":"Yang, S., Scherer, S.\u00a0A., Yi, X. & Zell, A. Multi-camera visual slam for autonomous navigation of micro aerial vehicles. Robot. Auton. Syst. (2017).","DOI":"10.1016\/j.robot.2017.03.018"},{"key":"25281_CR17","doi-asserted-by":"crossref","unstructured":"Patoliya, J., Mewada, H., Hassaballah, M., Khan, M.\u00a0A. & Kadry, S. A robust autonomous navigation and mapping system based on GPS and lidar data for unconstraint environment. Earth Sci. Inform. (2022).","DOI":"10.1007\/s12145-022-00791-x"},{"key":"25281_CR18","doi-asserted-by":"publisher","first-page":"107584","DOI":"10.1016\/j.compag.2022.107584","volume":"205","author":"Y Bai","year":"2023","unstructured":"Bai, Y. et al. Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review. Comput. Electron. Agric. 205, 107584 (2023).","journal-title":"Comput. Electron. Agric."},{"key":"25281_CR19","unstructured":"Taylor, R. et al. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085 (2022)."},{"key":"25281_CR20","doi-asserted-by":"crossref","unstructured":"Roumeliotis, K.\u00a0I. & Tselikas, N.\u00a0D. ChatGPT and open-AI models: A preliminary review. Future Internet 192 (2023).","DOI":"10.3390\/fi15060192"},{"key":"25281_CR21","unstructured":"Touvron, H. et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"25281_CR22","unstructured":"Zhao, W.\u00a0X. et al. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)."},{"key":"25281_CR23","doi-asserted-by":"crossref","unstructured":"Zeng, Y. et al. What matters in training a gpt4-style language model with multimodal inputs? In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 7930\u20137957 (2024).","DOI":"10.18653\/v1\/2024.naacl-long.440"},{"key":"25281_CR24","unstructured":"Wu, S., Fei, H., Qu, L., Ji, W. & Chua, T.-S. Next-GPT: Any-to-any multimodal LLM. arXiv preprint arXiv:2309.05519 (2023)."},{"key":"25281_CR25","doi-asserted-by":"crossref","unstructured":"Li, L., Zhang, Y. & Chen, L. Prompt distillation for efficient LLM-based recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1348\u20131357 (2023).","DOI":"10.1145\/3583780.3615017"},{"key":"25281_CR26","doi-asserted-by":"crossref","unstructured":"Xu, Q., Yan, J. & Cao, C. Emotional communication between chatbots and users: An empirical study on online customer service system. In International Conference on Human-Computer Interaction (Springer, 2022).","DOI":"10.1007\/978-3-031-05643-7_33"},{"key":"25281_CR27","doi-asserted-by":"crossref","unstructured":"Zamfirescu-Pereira, J., Wong, R.\u00a0Y., Hartmann, B. & Yang, Q. Why Johnny can\u2019t prompt: How non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023).","DOI":"10.1145\/3544548.3581388"},{"key":"25281_CR28","doi-asserted-by":"crossref","unstructured":"Zou, Z., Mubin, O., Alnajjar, F. & Ali, L. A pilot study of measuring emotional response and perception of LLM-generated questionnaire and human-generated questionnaires. Sci. Rep. (2024).","DOI":"10.1038\/s41598-024-53255-1"},{"key":"25281_CR29","unstructured":"Wang, J. et al. Large language models for robotics: Opportunities, challenges, and perspectives. arXiv preprint arXiv:2401.04334 (2024)."},{"key":"25281_CR30","unstructured":"Shah, D., Osi\u0144ski, B., Levine, S. et al. LM-NAV: Robotic navigation with large pre-trained models of language, vision, and action. In Conference on Robot Learning. 492\u2013504 (PMLR, 2023)."},{"key":"25281_CR31","doi-asserted-by":"crossref","unstructured":"Liu, H. et al. Enhancing the LLM-based robot manipulation through human-robot collaboration. arXiv preprint arXiv:2406.14097 (2024).","DOI":"10.1109\/LRA.2024.3415931"},{"key":"25281_CR32","unstructured":"Ouyang, L. et al. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems (2022)."},{"key":"25281_CR33","doi-asserted-by":"crossref","unstructured":"Islam, M.\u00a0J. et al. Mvs-slam: Enhanced multiview geometry for improved semantic rgbd slam in dynamic environments. J. Field Robot. (2023).","DOI":"10.1002\/rob.22248"},{"key":"25281_CR34","doi-asserted-by":"crossref","unstructured":"Islam, M.\u00a0J. et al. Dynamic object-aware slam: Yolov8 integration and enhanced multi-view geometry for reliable mapping in dynamic environments. In 2023 7th International Conference on Imaging, Signal Processing  (2023).","DOI":"10.1109\/ICISPC59567.2023.00013"},{"key":"25281_CR35","unstructured":"Xie, F., Zhang, J. & Schwertfeger, S. Intelligent lidar navigation: Leveraging external information and semantic maps with LLM as copilot. arXiv preprint arXiv:2409.08493 (2024)."},{"key":"25281_CR36","doi-asserted-by":"crossref","unstructured":"Yang, Y. et al. 3D-mem: 3D scene memory for embodied exploration and reasoning. In Proceedings of the Computer Vision and Pattern Recognition Conference. 17294\u201317303 (2025).","DOI":"10.1109\/CVPR52734.2025.01612"},{"key":"25281_CR37","doi-asserted-by":"crossref","unstructured":"Huang, C., Mees, O., Zeng, A. & Burgard, W. Visual language maps for robot navigation. arXiv preprint arXiv:2210.05714 (2022).","DOI":"10.1109\/ICRA48891.2023.10160969"},{"key":"25281_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, G., Hong, Y. & Wu, Q. Navgpt: Explicit reasoning in vision-and-language navigation with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. 7641\u20137649 (2024).","DOI":"10.1609\/aaai.v38i7.28597"},{"key":"25281_CR39","unstructured":"Halama, M., Nowak, S., Po\u0142ys, K., Biernacki, P. & Sobczak, \u0141. Semantic mapping with LLM - IITIS pan. YouTube video. https:\/\/youtu.be\/qXCpKFSEEBs (2025)."},{"key":"25281_CR40","doi-asserted-by":"crossref","unstructured":"Deng, J. et al. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. 248\u2013255 (IEEE, 2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"25281_CR41","doi-asserted-by":"crossref","unstructured":"Caesar, H., Uijlings, J. & Ferrari, V. Coco-stuff: Thing and stuff classes in context. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1209\u20131218 (2018).","DOI":"10.1109\/CVPR.2018.00132"},{"key":"25281_CR42","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1017\/S1351324916000334","volume":"23","author":"KW Church","year":"2017","unstructured":"Church, K. W. Word2vec. Nat. Lang. Eng. 23, 155\u2013162 (2017).","journal-title":"Nat. Lang. Eng."},{"key":"25281_CR43","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R. & Manning, C.\u00a0D. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 1532\u20131543 (2014).","DOI":"10.3115\/v1\/D14-1162"},{"key":"25281_CR44","unstructured":"Joulin, A. Fasttext. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651 (2016)."},{"key":"25281_CR45","unstructured":"Alsentzer, E. et al. Publicly available clinical bert embeddings. arXiv preprint arXiv:1904.03323 (2019)."},{"key":"25281_CR46","unstructured":"Chan, S. et al. Human and LLM-based voice assistant interaction: An analytical framework for user verbal and nonverbal behaviors. arXiv preprint arXiv:2408.16465 (2024)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25281-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25281-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25281-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T12:26:39Z","timestamp":1763987199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-25281-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,24]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["25281"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-25281-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,24]]},"assertion":[{"value":"18 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"41519"}}