{"title":"基于激光雷达融合的定位系统","authors":"Junchang Zhou, Changjun He, Jie Fang","doi":"10.1109/IWECAI50956.2020.00013","DOIUrl":null,"url":null,"abstract":"Aiming at the problems of slow positioning speed and information loss in the process of autonomous navigation of robots, we propose the adaptive Monte Carlo Localization (AMCL) algorithm based on lidar data fusion under the Robot Operating System (ROS) development system, realizing the robot for faster positioning and navigation.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Positioning System Based on Lidar Fusion\",\"authors\":\"Junchang Zhou, Changjun He, Jie Fang\",\"doi\":\"10.1109/IWECAI50956.2020.00013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problems of slow positioning speed and information loss in the process of autonomous navigation of robots, we propose the adaptive Monte Carlo Localization (AMCL) algorithm based on lidar data fusion under the Robot Operating System (ROS) development system, realizing the robot for faster positioning and navigation.\",\"PeriodicalId\":364789,\"journal\":{\"name\":\"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWECAI50956.2020.00013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWECAI50956.2020.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Aiming at the problems of slow positioning speed and information loss in the process of autonomous navigation of robots, we propose the adaptive Monte Carlo Localization (AMCL) algorithm based on lidar data fusion under the Robot Operating System (ROS) development system, realizing the robot for faster positioning and navigation.