{"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/offline-and-online-calibration-of-mobile","title":"Offline and Online calibration of Mobile Robot and SLAM Device for Navigation","arxiv_id":"1804.04817","date":"2018-04-13","proceeding":null,"authors":["Ryoichi Ishikawa","Takeshi Oishi","Katsushi Ikeuchi"],"abstract":"Robot navigation technology is required to accomplish difficult tasks in\nvarious environments. In navigation, it is necessary to know the information of\nthe external environments and the state of the robot under the environment. On\nthe other hand, various studies have been done on SLAM technology, which is\nalso used for navigation, but also applied to devices for Mixed Reality and the\nlike.\n  In this paper, we propose a robot-device calibration method for navigation\nwith a device using SLAM technology on a robot. The calibration is performed by\nusing the position and orientation information given by the robot and the\ndevice. In the calibration, the most efficient way of movement is clarified\naccording to the restriction of the robot movement. Furthermore, we also show a\nmethod to dynamically correct the position and orientation of the robot so that\nthe information of the external environment and the shape information of the\nrobot maintain consistency in order to reduce the dynamic error occurring\nduring navigation.\n  Our method can be easily used for various kinds of robots and localization\nwith sufficient precision for navigation is possible with offline calibration\nand online position correction. In the experiments, we confirm the parameters\nobtained by two types of offline calibration according to the degree of freedom\nof robot movement and validate the effectiveness of online correction method by\nplotting localized position error during robot's intense movement. Finally, we\nshow the demonstration of navigation using SLAM device.","url_abs":"http://arxiv.org/abs/1804.04817v1","url_pdf":"http://arxiv.org/pdf/1804.04817v1.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":"offline-and-online-calibration-of-mobile","repo_url":"https://github.com/cln515/HoloLensRobotNav","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"mixed-reality","task_name":"Mixed Reality"},{"task_slug":null,"task_name":"Position"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}