{"title":"RGB-D SLAM系统的一些改进","authors":"Hieu Pham Quang, N. Ly","doi":"10.1109/RIVF.2015.7049884","DOIUrl":null,"url":null,"abstract":"RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robot sensor. In this work, we present an RGB-D SLAM system using the Microsoft Kinect. The proposed system is a full 6DoF (Degrees of Freedom) SLAM system which can estimate camera trajectory and reconstruct a 3D model of the environment in real-time. Unlike traditional filtering-based approaches, our system optimizes the entire trajectory by the use of graph optimization. We achieve better accuracy than a previous system by employing key-frame matching instead of frame-to-frame matching. We evaluate our system on a published dataset. The results demonstrate that our system can handle unrestricted camera movements in indoor settings.","PeriodicalId":166971,"journal":{"name":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Some improvements in the RGB-D SLAM system\",\"authors\":\"Hieu Pham Quang, N. Ly\",\"doi\":\"10.1109/RIVF.2015.7049884\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robot sensor. In this work, we present an RGB-D SLAM system using the Microsoft Kinect. The proposed system is a full 6DoF (Degrees of Freedom) SLAM system which can estimate camera trajectory and reconstruct a 3D model of the environment in real-time. Unlike traditional filtering-based approaches, our system optimizes the entire trajectory by the use of graph optimization. We achieve better accuracy than a previous system by employing key-frame matching instead of frame-to-frame matching. We evaluate our system on a published dataset. The results demonstrate that our system can handle unrestricted camera movements in indoor settings.\",\"PeriodicalId\":166971,\"journal\":{\"name\":\"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RIVF.2015.7049884\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RIVF.2015.7049884","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robot sensor. In this work, we present an RGB-D SLAM system using the Microsoft Kinect. The proposed system is a full 6DoF (Degrees of Freedom) SLAM system which can estimate camera trajectory and reconstruct a 3D model of the environment in real-time. Unlike traditional filtering-based approaches, our system optimizes the entire trajectory by the use of graph optimization. We achieve better accuracy than a previous system by employing key-frame matching instead of frame-to-frame matching. We evaluate our system on a published dataset. The results demonstrate that our system can handle unrestricted camera movements in indoor settings.