{"title":"室内环境下移动机器人SLAM的灰色动态EKF","authors":"Peng-Cheng Wang, Qibin Zhang, Zonghai Chen","doi":"10.1109/RAM.2013.6758557","DOIUrl":null,"url":null,"abstract":"The Gray-dynamic EKF (GEKF) algorithm is proposed to estimate the states of a mobile robot in an indoor environment. First, the gray prediction theory is adopted to predict the states of a mobile robot and the feature positions in the environment; next, based on the predictions, a mobile robot system model is built dynamically; then, the GEKF is used to estimate the mobile robot states and the feature positions. Experimental results show that the GEKF can achieve almost the same estimation accuracy with EKF, while without the need of a fixed system model. To improve the head direction estimation accuracy of the mobile robot, a head direction match algorithm is proposed, and relatively better results are shown by experiments.","PeriodicalId":287085,"journal":{"name":"2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Gray-dynamic EKF for mobile robot SLAM in indoor environment\",\"authors\":\"Peng-Cheng Wang, Qibin Zhang, Zonghai Chen\",\"doi\":\"10.1109/RAM.2013.6758557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Gray-dynamic EKF (GEKF) algorithm is proposed to estimate the states of a mobile robot in an indoor environment. First, the gray prediction theory is adopted to predict the states of a mobile robot and the feature positions in the environment; next, based on the predictions, a mobile robot system model is built dynamically; then, the GEKF is used to estimate the mobile robot states and the feature positions. Experimental results show that the GEKF can achieve almost the same estimation accuracy with EKF, while without the need of a fixed system model. To improve the head direction estimation accuracy of the mobile robot, a head direction match algorithm is proposed, and relatively better results are shown by experiments.\",\"PeriodicalId\":287085,\"journal\":{\"name\":\"2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAM.2013.6758557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAM.2013.6758557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gray-dynamic EKF for mobile robot SLAM in indoor environment
The Gray-dynamic EKF (GEKF) algorithm is proposed to estimate the states of a mobile robot in an indoor environment. First, the gray prediction theory is adopted to predict the states of a mobile robot and the feature positions in the environment; next, based on the predictions, a mobile robot system model is built dynamically; then, the GEKF is used to estimate the mobile robot states and the feature positions. Experimental results show that the GEKF can achieve almost the same estimation accuracy with EKF, while without the need of a fixed system model. To improve the head direction estimation accuracy of the mobile robot, a head direction match algorithm is proposed, and relatively better results are shown by experiments.