{"title":"传感器云:用于人机协作的多模态传感器数据实时处理的框架","authors":"Alexander Poeppel, Christian Eymüller, W. Reif","doi":"10.1109/ICARA56516.2023.10125740","DOIUrl":null,"url":null,"abstract":"Human-robot-collaboration (HRC) requires fast and reliable sensor data to ensure the safety of humans in the workspace. Current solutions for processing multi-modal sensor data in HRC are either highly performant in specific scenarios or offer more flexibility at the cost of decreased performance. Our GPU accelerated SensorClouds framework, however, combines both high flexibility and real-time performance. The architecture aids developers in quickly implementing complex HRC applications with multiple sensors by encapsulating all functionality into reusable modules. The resulting pipeline is optimized by the framework and executed in real-time.","PeriodicalId":443572,"journal":{"name":"2023 9th International Conference on Automation, Robotics and Applications (ICARA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SensorClouds: A Framework for Real-Time Processing of Multi-modal Sensor Data for Human-Robot-Collaboration\",\"authors\":\"Alexander Poeppel, Christian Eymüller, W. Reif\",\"doi\":\"10.1109/ICARA56516.2023.10125740\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Human-robot-collaboration (HRC) requires fast and reliable sensor data to ensure the safety of humans in the workspace. Current solutions for processing multi-modal sensor data in HRC are either highly performant in specific scenarios or offer more flexibility at the cost of decreased performance. Our GPU accelerated SensorClouds framework, however, combines both high flexibility and real-time performance. The architecture aids developers in quickly implementing complex HRC applications with multiple sensors by encapsulating all functionality into reusable modules. The resulting pipeline is optimized by the framework and executed in real-time.\",\"PeriodicalId\":443572,\"journal\":{\"name\":\"2023 9th International Conference on Automation, Robotics and Applications (ICARA)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 9th International Conference on Automation, Robotics and Applications (ICARA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARA56516.2023.10125740\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 9th International Conference on Automation, Robotics and Applications (ICARA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARA56516.2023.10125740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SensorClouds: A Framework for Real-Time Processing of Multi-modal Sensor Data for Human-Robot-Collaboration
Human-robot-collaboration (HRC) requires fast and reliable sensor data to ensure the safety of humans in the workspace. Current solutions for processing multi-modal sensor data in HRC are either highly performant in specific scenarios or offer more flexibility at the cost of decreased performance. Our GPU accelerated SensorClouds framework, however, combines both high flexibility and real-time performance. The architecture aids developers in quickly implementing complex HRC applications with multiple sensors by encapsulating all functionality into reusable modules. The resulting pipeline is optimized by the framework and executed in real-time.