{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/online-spatial-concept-and-lexical","title":"Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping","arxiv_id":"1704.04664","date":"2017-04-15","proceeding":null,"authors":["Akira Taniguchi","Yoshinobu Hagiwara","Tadahiro Taniguchi","Tetsunari Inamura"],"abstract":"In this paper, we propose an online learning algorithm based on a\nRao-Blackwellized particle filter for spatial concept acquisition and mapping.\nWe have proposed a nonparametric Bayesian spatial concept acquisition model\n(SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in\nthe theoretical framework of the Bayesian generative model. The proposed method\ncan simultaneously learn place categories and lexicons while incrementally\ngenerating an environmental map. Furthermore, the proposed method has scene\nimage features and a language model added to SpCoA. In the experiments, we\ntested online learning of spatial concepts and environmental maps in a novel\nenvironment of which the robot did not have a map. Then, we evaluated the\nresults of online learning of spatial concepts and lexical acquisition. The\nexperimental results demonstrated that the robot was able to more accurately\nlearn the relationships between words and the place in the environmental map\nincrementally by using the proposed method.","url_abs":"http://arxiv.org/abs/1704.04664v2","url_pdf":"http://arxiv.org/pdf/1704.04664v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"online-spatial-concept-and-lexical","repo_url":"https://github.com/EmergentSystemLabStudent/SpCoSLAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"online-spatial-concept-and-lexical","repo_url":"https://github.com/EmergentSystemLabStudent/SpCoSLAM_Lets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"online-spatial-concept-and-lexical","repo_url":"https://github.com/a-taniguchi/SpCoSLAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"online-spatial-concept-and-lexical","repo_url":"https://github.com/a-taniguchi/SpCoSLAM2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"online-spatial-concept-and-lexical","repo_url":"https://github.com/a-taniguchi/SpCoSLAM_evaluation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"simultaneous-localization-and-mapping","task_name":"Simultaneous Localization and Mapping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}