{"title":"基于货架检测的仓库人员定位","authors":"Karel Kosnar, Gaël Écorchard, L. Preucil","doi":"10.1109/ECMR.2019.8870913","DOIUrl":null,"url":null,"abstract":"This paper proposes a method for human localization in the automated warehouses. As the environment is highly dynamic due to changes in the racks content as well as the position of the rack, the proposed method uses the racks as landmarks for the localization. The fixed structure of the shelves and stands of the rack is used to detect the rack itself regardless of the content. The structure is coded as the graph and the detection is performed by finding the isomorphism between the rack model and the detected structure of the environment. The relative position is obtained by solving a PnP problem. The global position of the rack is gathered from the warehouse system by reading the marker identifying the rack. Experiments show that the localization has a mean error of 15 cm and is much more robust than the localization based on the marker itself.","PeriodicalId":435630,"journal":{"name":"2019 European Conference on Mobile Robots (ECMR)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Localization of Humans in Warehouse based on Rack Detection\",\"authors\":\"Karel Kosnar, Gaël Écorchard, L. Preucil\",\"doi\":\"10.1109/ECMR.2019.8870913\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a method for human localization in the automated warehouses. As the environment is highly dynamic due to changes in the racks content as well as the position of the rack, the proposed method uses the racks as landmarks for the localization. The fixed structure of the shelves and stands of the rack is used to detect the rack itself regardless of the content. The structure is coded as the graph and the detection is performed by finding the isomorphism between the rack model and the detected structure of the environment. The relative position is obtained by solving a PnP problem. The global position of the rack is gathered from the warehouse system by reading the marker identifying the rack. Experiments show that the localization has a mean error of 15 cm and is much more robust than the localization based on the marker itself.\",\"PeriodicalId\":435630,\"journal\":{\"name\":\"2019 European Conference on Mobile Robots (ECMR)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 European Conference on Mobile Robots (ECMR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECMR.2019.8870913\",\"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 European Conference on Mobile Robots (ECMR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECMR.2019.8870913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Localization of Humans in Warehouse based on Rack Detection
This paper proposes a method for human localization in the automated warehouses. As the environment is highly dynamic due to changes in the racks content as well as the position of the rack, the proposed method uses the racks as landmarks for the localization. The fixed structure of the shelves and stands of the rack is used to detect the rack itself regardless of the content. The structure is coded as the graph and the detection is performed by finding the isomorphism between the rack model and the detected structure of the environment. The relative position is obtained by solving a PnP problem. The global position of the rack is gathered from the warehouse system by reading the marker identifying the rack. Experiments show that the localization has a mean error of 15 cm and is much more robust than the localization based on the marker itself.