T. T. Hoang, P. M. Duong, N. T. T. Van, D. A. Viet, T. Q. Vinh
{"title":"Development of an EKF-based localization algorithm using compass sensor and LRF","authors":"T. T. Hoang, P. M. Duong, N. T. T. Van, D. A. Viet, T. Q. Vinh","doi":"10.1109/ICARCV.2012.6485182","DOIUrl":null,"url":null,"abstract":"This paper presents the implementation of a perceptual system for a mobile robot. The system is designed and installed with modern sensors and multi-point communication channels. The goal is to equip the robot with a high level of perception to support a wide range of navigating applications including Internet-based telecontrol, semi-autonomy, and autonomy. Due to uncertainties of acquiring data, a sensor fusion model is developed, in which heterogeneous measured data including odometry, compass heading and laser range is combined to get an optimal estimate in a statistical sense. The combination is carried out by an extended Kalman filter. Experimental results indicate that based on the system, the robot localization is enhanced and sufficient for navigation tasks.","PeriodicalId":441236,"journal":{"name":"2012 12th International Conference on Control Automation Robotics & Vision (ICARCV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 12th International Conference on Control Automation Robotics & Vision (ICARCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2012.6485182","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
This paper presents the implementation of a perceptual system for a mobile robot. The system is designed and installed with modern sensors and multi-point communication channels. The goal is to equip the robot with a high level of perception to support a wide range of navigating applications including Internet-based telecontrol, semi-autonomy, and autonomy. Due to uncertainties of acquiring data, a sensor fusion model is developed, in which heterogeneous measured data including odometry, compass heading and laser range is combined to get an optimal estimate in a statistical sense. The combination is carried out by an extended Kalman filter. Experimental results indicate that based on the system, the robot localization is enhanced and sufficient for navigation tasks.