{"title":"The Implementation of the SLAM Method in ROS System and Underwater Robot","authors":"Fei Suo, Jiaqi Lv, Zhan Wang","doi":"10.1109/ICUS55513.2022.9986897","DOIUrl":null,"url":null,"abstract":"Autonomous localization and map construction of underwater robots in unknown environments is a very important research area. Visual SLAM technology is based on visual processing algorithms and can accomplish this task at a low cost. This paper focuses on a set of underwater robot visual SLAM experimental process. Based on ROS system, Mono visual SLAM experiments are performed on UWSim simulation environment and real robot respectively by using mature ORB-SLAM2 algorithm. The experimental results show that this method can accurately build the sparse point cloud map of the environment under the condition of sufficient environmental feature points.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Unmanned Systems (ICUS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUS55513.2022.9986897","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
Autonomous localization and map construction of underwater robots in unknown environments is a very important research area. Visual SLAM technology is based on visual processing algorithms and can accomplish this task at a low cost. This paper focuses on a set of underwater robot visual SLAM experimental process. Based on ROS system, Mono visual SLAM experiments are performed on UWSim simulation environment and real robot respectively by using mature ORB-SLAM2 algorithm. The experimental results show that this method can accurately build the sparse point cloud map of the environment under the condition of sufficient environmental feature points.
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