Ji-il Park, Minseong Choi, Seungho Han, Yeongseok Lee, J. Cho, Hyoseo Choi, Min-Yyeong Cho, Minyoung Lee, Kyung-Soo Kim
{"title":"基于ros的多地点多天气条件下自动驾驶数据采集小型无人平台的研制","authors":"Ji-il Park, Minseong Choi, Seungho Han, Yeongseok Lee, J. Cho, Hyoseo Choi, Min-Yyeong Cho, Minyoung Lee, Kyung-Soo Kim","doi":"10.1109/COMPSAC54236.2022.00013","DOIUrl":null,"url":null,"abstract":"As autonomous driving research has actively pro-gressed, software for autonomous vehicles and embedded systems such as Apollo and AutoWare have also been developed, providing a complete set of self-driving modules, including perception, localization and mapping, path planning, prediction, decision making, and control. Most of the researchers currently use these software programs, but many researchers have also studied autonomous driving based on the middleware software termed robot operating system (ROS) before such software was released, especially in academia. Accordingly, we intend to develop ROS-based unmanned RC car equipped with autonomous driving sensors such as LiDAR, radar, VIS/IR cameras, GPS, and IMUs that can provide ROS-based datasets to researchers studying self-driving cars and robots using ROS. In addition, unlike conventional datasets, we acquire dataset not only on road but also pedestrian paths that can be used in both vehicles and robots and provides extreme environmental datasets such as snowfall environments. In this sense, the ROS dataset we created will be helpful to researchers studying autonomous vehicles and robots by using ROS.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"274 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Development of ROS-based Small Unmanned Platform for Acquiring Autonomous Driving Dataset in Various Places and Weather Conditions\",\"authors\":\"Ji-il Park, Minseong Choi, Seungho Han, Yeongseok Lee, J. Cho, Hyoseo Choi, Min-Yyeong Cho, Minyoung Lee, Kyung-Soo Kim\",\"doi\":\"10.1109/COMPSAC54236.2022.00013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As autonomous driving research has actively pro-gressed, software for autonomous vehicles and embedded systems such as Apollo and AutoWare have also been developed, providing a complete set of self-driving modules, including perception, localization and mapping, path planning, prediction, decision making, and control. Most of the researchers currently use these software programs, but many researchers have also studied autonomous driving based on the middleware software termed robot operating system (ROS) before such software was released, especially in academia. Accordingly, we intend to develop ROS-based unmanned RC car equipped with autonomous driving sensors such as LiDAR, radar, VIS/IR cameras, GPS, and IMUs that can provide ROS-based datasets to researchers studying self-driving cars and robots using ROS. In addition, unlike conventional datasets, we acquire dataset not only on road but also pedestrian paths that can be used in both vehicles and robots and provides extreme environmental datasets such as snowfall environments. In this sense, the ROS dataset we created will be helpful to researchers studying autonomous vehicles and robots by using ROS.\",\"PeriodicalId\":330838,\"journal\":{\"name\":\"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"volume\":\"274 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPSAC54236.2022.00013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPSAC54236.2022.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of ROS-based Small Unmanned Platform for Acquiring Autonomous Driving Dataset in Various Places and Weather Conditions
As autonomous driving research has actively pro-gressed, software for autonomous vehicles and embedded systems such as Apollo and AutoWare have also been developed, providing a complete set of self-driving modules, including perception, localization and mapping, path planning, prediction, decision making, and control. Most of the researchers currently use these software programs, but many researchers have also studied autonomous driving based on the middleware software termed robot operating system (ROS) before such software was released, especially in academia. Accordingly, we intend to develop ROS-based unmanned RC car equipped with autonomous driving sensors such as LiDAR, radar, VIS/IR cameras, GPS, and IMUs that can provide ROS-based datasets to researchers studying self-driving cars and robots using ROS. In addition, unlike conventional datasets, we acquire dataset not only on road but also pedestrian paths that can be used in both vehicles and robots and provides extreme environmental datasets such as snowfall environments. In this sense, the ROS dataset we created will be helpful to researchers studying autonomous vehicles and robots by using ROS.