{"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/slam-with-objects-using-a-nonparametric-pose","title":"SLAM with Objects using a Nonparametric Pose Graph","arxiv_id":"1704.05959","date":"2017-04-19","proceeding":null,"authors":["Beipeng Mu","Shih-Yuan Liu","Liam Paull","John Leonard","Jonathan How"],"abstract":"Mapping and self-localization in unknown environments are fundamental\ncapabilities in many robotic applications. These tasks typically involve the\nidentification of objects as unique features or landmarks, which requires the\nobjects both to be detected and then assigned a unique identifier that can be\nmaintained when viewed from different perspectives and in different images. The\n\\textit{data association} and \\textit{simultaneous localization and mapping}\n(SLAM) problems are, individually, well-studied in the literature. But these\ntwo problems are inherently tightly coupled, and that has not been\nwell-addressed. Without accurate SLAM, possible data associations are\ncombinatorial and become intractable easily. Without accurate data association,\nthe error of SLAM algorithms diverge easily. This paper proposes a novel\nnonparametric pose graph that models data association and SLAM in a single\nframework. An algorithm is further introduced to alternate between inferring\ndata association and performing SLAM. Experimental results show that our\napproach has the new capability of associating object detections and localizing\nobjects at the same time, leading to significantly better performance on both\nthe data association and SLAM problems than achieved by considering only one\nand ignoring imperfections in the other.","url_abs":"http://arxiv.org/abs/1704.05959v1","url_pdf":"http://arxiv.org/pdf/1704.05959v1.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":"slam-with-objects-using-a-nonparametric-pose","repo_url":"https://github.com/BeipengMu/objectSLAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"slam-with-objects-using-a-nonparametric-pose","repo_url":"https://github.com/tiev-tongji/quadric_slam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"simultaneous-localization-and-mapping","task_name":"Simultaneous Localization and Mapping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.05959","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}