{"title":"基于地标的移动机器人定位方法","authors":"M. Oussalah, H. Maaref, C. Barret","doi":"10.1109/IROS.1997.655111","DOIUrl":null,"url":null,"abstract":"In order to develop an autonomous system, the problem of determining accurately the position of the robot has to be solved. In this paper we deal with landmark-based method where the location of the landmark is known with regard to the environment. These landmarks are composed of a set of infrared LEDs that help the mobile robot to accurately increase the accuracy of the estimation of its local position as given by the odometer sensor. Odometry reading and exteroceptive sensors are modelled in the setting of the possibility theory. The possibility distributions relative to different sensors are turned into the same referential, and then combined by means of an adaptive combination rule.","PeriodicalId":408848,"journal":{"name":"Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Positioning of a mobile robot with landmark-based method\",\"authors\":\"M. Oussalah, H. Maaref, C. Barret\",\"doi\":\"10.1109/IROS.1997.655111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to develop an autonomous system, the problem of determining accurately the position of the robot has to be solved. In this paper we deal with landmark-based method where the location of the landmark is known with regard to the environment. These landmarks are composed of a set of infrared LEDs that help the mobile robot to accurately increase the accuracy of the estimation of its local position as given by the odometer sensor. Odometry reading and exteroceptive sensors are modelled in the setting of the possibility theory. The possibility distributions relative to different sensors are turned into the same referential, and then combined by means of an adaptive combination rule.\",\"PeriodicalId\":408848,\"journal\":{\"name\":\"Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IROS.1997.655111\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.1997.655111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Positioning of a mobile robot with landmark-based method
In order to develop an autonomous system, the problem of determining accurately the position of the robot has to be solved. In this paper we deal with landmark-based method where the location of the landmark is known with regard to the environment. These landmarks are composed of a set of infrared LEDs that help the mobile robot to accurately increase the accuracy of the estimation of its local position as given by the odometer sensor. Odometry reading and exteroceptive sensors are modelled in the setting of the possibility theory. The possibility distributions relative to different sensors are turned into the same referential, and then combined by means of an adaptive combination rule.