{"title":"基于卡尔曼滤波的UWB/GPS传感器融合户外自主机器人","authors":"Junhyeok Shin, Hoeryoung Jung","doi":"10.23919/ICCAS55662.2022.10003948","DOIUrl":null,"url":null,"abstract":"Outdoor autonomous robots generally use GPS sensors for position measurement. GPS sensors have poor positioning accuracy in complex environments (etc. tunnels, forests, and dense building areas). In this work, UWB/GPS fusion is performed using the Kalman filter-based sensor fusion method. Using Gazebo simulations, GPS and the proposed method were compared. The proposed method showed improved positional accuracy over the use of single-step GPS","PeriodicalId":129856,"journal":{"name":"2022 22nd International Conference on Control, Automation and Systems (ICCAS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"UWB/GPS Sensor Fusion Using Kalman Filter for Outdoor Autonomous Robot\",\"authors\":\"Junhyeok Shin, Hoeryoung Jung\",\"doi\":\"10.23919/ICCAS55662.2022.10003948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Outdoor autonomous robots generally use GPS sensors for position measurement. GPS sensors have poor positioning accuracy in complex environments (etc. tunnels, forests, and dense building areas). In this work, UWB/GPS fusion is performed using the Kalman filter-based sensor fusion method. Using Gazebo simulations, GPS and the proposed method were compared. The proposed method showed improved positional accuracy over the use of single-step GPS\",\"PeriodicalId\":129856,\"journal\":{\"name\":\"2022 22nd International Conference on Control, Automation and Systems (ICCAS)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 22nd International Conference on Control, Automation and Systems (ICCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ICCAS55662.2022.10003948\",\"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 22nd International Conference on Control, Automation and Systems (ICCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICCAS55662.2022.10003948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
UWB/GPS Sensor Fusion Using Kalman Filter for Outdoor Autonomous Robot
Outdoor autonomous robots generally use GPS sensors for position measurement. GPS sensors have poor positioning accuracy in complex environments (etc. tunnels, forests, and dense building areas). In this work, UWB/GPS fusion is performed using the Kalman filter-based sensor fusion method. Using Gazebo simulations, GPS and the proposed method were compared. The proposed method showed improved positional accuracy over the use of single-step GPS
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