{"title":"移动机器人与SLAM导航装置的动态标定","authors":"Ryoichi Ishikawa, Takeshi Oishi, K. Ikeuchi","doi":"10.1109/RO-MAN46459.2019.8956356","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a dynamic calibration between a mobile robot and a device using simultaneous localization and mapping (SLAM) technology, which we termed as the SLAM device, for a robot navigation system. The navigation framework assumes loose mounting of SLAM device for easy use and requires an online adjustment to remove localization errors. The online adjustment method dynamically corrects not only the calibration errors between the SLAM device and the part of the robot to which the device is attached but also the robot encoder errors by calibrating the whole body of the robot. The online adjustment assumes that the information of the external environment and shape information of the robot are consistent. In addition to the online adjustment, we also present an offline calibration between a robot and device. The offline calibration is motion-based and we clarify the most efficient method based on the number of degrees-of-freedom of the robot movement. Our method can be easily used for various types of robots with sufficiently precise localization for navigation. In the experiments, we confirm the parameters obtained via two types of offline calibration based on the degree of freedom of robot movement. We also validate the effectiveness of the online adjustment method by plotting localized position errors during a robots intense movement. Finally, we demonstrate the navigation using a SLAM device.","PeriodicalId":286478,"journal":{"name":"2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Dynamic Calibration between a Mobile Robot and SLAM Device for Navigation\",\"authors\":\"Ryoichi Ishikawa, Takeshi Oishi, K. Ikeuchi\",\"doi\":\"10.1109/RO-MAN46459.2019.8956356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a dynamic calibration between a mobile robot and a device using simultaneous localization and mapping (SLAM) technology, which we termed as the SLAM device, for a robot navigation system. The navigation framework assumes loose mounting of SLAM device for easy use and requires an online adjustment to remove localization errors. The online adjustment method dynamically corrects not only the calibration errors between the SLAM device and the part of the robot to which the device is attached but also the robot encoder errors by calibrating the whole body of the robot. The online adjustment assumes that the information of the external environment and shape information of the robot are consistent. In addition to the online adjustment, we also present an offline calibration between a robot and device. The offline calibration is motion-based and we clarify the most efficient method based on the number of degrees-of-freedom of the robot movement. Our method can be easily used for various types of robots with sufficiently precise localization for navigation. In the experiments, we confirm the parameters obtained via two types of offline calibration based on the degree of freedom of robot movement. We also validate the effectiveness of the online adjustment method by plotting localized position errors during a robots intense movement. Finally, we demonstrate the navigation using a SLAM device.\",\"PeriodicalId\":286478,\"journal\":{\"name\":\"2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RO-MAN46459.2019.8956356\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RO-MAN46459.2019.8956356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic Calibration between a Mobile Robot and SLAM Device for Navigation
In this paper, we propose a dynamic calibration between a mobile robot and a device using simultaneous localization and mapping (SLAM) technology, which we termed as the SLAM device, for a robot navigation system. The navigation framework assumes loose mounting of SLAM device for easy use and requires an online adjustment to remove localization errors. The online adjustment method dynamically corrects not only the calibration errors between the SLAM device and the part of the robot to which the device is attached but also the robot encoder errors by calibrating the whole body of the robot. The online adjustment assumes that the information of the external environment and shape information of the robot are consistent. In addition to the online adjustment, we also present an offline calibration between a robot and device. The offline calibration is motion-based and we clarify the most efficient method based on the number of degrees-of-freedom of the robot movement. Our method can be easily used for various types of robots with sufficiently precise localization for navigation. In the experiments, we confirm the parameters obtained via two types of offline calibration based on the degree of freedom of robot movement. We also validate the effectiveness of the online adjustment method by plotting localized position errors during a robots intense movement. Finally, we demonstrate the navigation using a SLAM device.