{"title":"结合视觉CNN特征和子地图的SLAM闭环检测","authors":"Hao Qin, May Huang, Jian Cao, Xing Zhang","doi":"10.1109/ICCAR.2018.8384713","DOIUrl":null,"url":null,"abstract":"Using simultaneous localization and mapping (SLAM) with 2D LIDAR is an efficient approach for robots to build a floor plan, but it is sensitive to the environment. For improving the accuracy, we match LIDAR data with sub-maps. Furthermore, we convert LIDAR data to images and merge with camera data for image matching. Combining the two approaches, we achieve robust and accurate loop closure detection. The descriptors generated by CNN model will be used as features to image matching for accuracy improvement.","PeriodicalId":106624,"journal":{"name":"2018 4th International Conference on Control, Automation and Robotics (ICCAR)","volume":"288 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Loop closure detection in SLAM by combining visual CNN features and submaps\",\"authors\":\"Hao Qin, May Huang, Jian Cao, Xing Zhang\",\"doi\":\"10.1109/ICCAR.2018.8384713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Using simultaneous localization and mapping (SLAM) with 2D LIDAR is an efficient approach for robots to build a floor plan, but it is sensitive to the environment. For improving the accuracy, we match LIDAR data with sub-maps. Furthermore, we convert LIDAR data to images and merge with camera data for image matching. Combining the two approaches, we achieve robust and accurate loop closure detection. The descriptors generated by CNN model will be used as features to image matching for accuracy improvement.\",\"PeriodicalId\":106624,\"journal\":{\"name\":\"2018 4th International Conference on Control, Automation and Robotics (ICCAR)\",\"volume\":\"288 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 4th International Conference on Control, Automation and Robotics (ICCAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAR.2018.8384713\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 4th International Conference on Control, Automation and Robotics (ICCAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAR.2018.8384713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Loop closure detection in SLAM by combining visual CNN features and submaps
Using simultaneous localization and mapping (SLAM) with 2D LIDAR is an efficient approach for robots to build a floor plan, but it is sensitive to the environment. For improving the accuracy, we match LIDAR data with sub-maps. Furthermore, we convert LIDAR data to images and merge with camera data for image matching. Combining the two approaches, we achieve robust and accurate loop closure detection. The descriptors generated by CNN model will be used as features to image matching for accuracy improvement.
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