{"title":"基于视频图像的远程全方位车辆平台SLAM配置","authors":"P. I. Chang, Y. Shi, S. C. Fan-Chiang, C. Lan","doi":"10.1109/ARIS50834.2020.9205779","DOIUrl":null,"url":null,"abstract":"This paper attempts to fully reconstruct a local mapping for robotic vehicle platforms, by use of 3D commercial camera. The reconstructed SLAM is verified by the global positioning of the surrounding with a-priori knowledge. While the whole omni-directional vehicle is designed and built in-house to maximize utility of all the signals available from the system. The mapping error for 2D for localization is estimated at 5% showing promise for this approach.","PeriodicalId":423389,"journal":{"name":"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"SLAM Configuration from Video Images for Remote Omni-direction Vehicle Platform\",\"authors\":\"P. I. Chang, Y. Shi, S. C. Fan-Chiang, C. Lan\",\"doi\":\"10.1109/ARIS50834.2020.9205779\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper attempts to fully reconstruct a local mapping for robotic vehicle platforms, by use of 3D commercial camera. The reconstructed SLAM is verified by the global positioning of the surrounding with a-priori knowledge. While the whole omni-directional vehicle is designed and built in-house to maximize utility of all the signals available from the system. The mapping error for 2D for localization is estimated at 5% showing promise for this approach.\",\"PeriodicalId\":423389,\"journal\":{\"name\":\"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ARIS50834.2020.9205779\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ARIS50834.2020.9205779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SLAM Configuration from Video Images for Remote Omni-direction Vehicle Platform
This paper attempts to fully reconstruct a local mapping for robotic vehicle platforms, by use of 3D commercial camera. The reconstructed SLAM is verified by the global positioning of the surrounding with a-priori knowledge. While the whole omni-directional vehicle is designed and built in-house to maximize utility of all the signals available from the system. The mapping error for 2D for localization is estimated at 5% showing promise for this approach.
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