Browse > Article
http://dx.doi.org/10.13067/JKIECS.2012.7.2.381

Particle filter for Correction of GPS location data of a mobile robot  

Noh, Sung-Woo (조선대학교 정보통신공학과)
Kim, Tae-Gyun (조선대학교 제어계측공학과)
Ko, Nak-Yong (조선대학교 제어계측로봇공학과)
Bae, Young-Chul (전남대학교 전기.전자통신.컴퓨터 공학부)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.7, no.2, 2012 , pp. 381-389 More about this Journal
Abstract
This paper proposes a method which corrects location data of GPS for navigation of outdoor mobile robot. The method uses a Bayesian filter approach called the particle filter(PF). The method iterates two procedures: prediction and correction. The prediction procedure calculates robot location based on translational and rotational velocity data given by the robot command. It incorporates uncertainty into the predicted robot location by adding uncertainty to translational and rotational velocity command. Using the sensor characteristics of the GPS, the belief that a particle assumes true location of the robot is calculated. The resampling from the particles based on the belief constitutes the correction procedure. Since usual GPS data includes abrupt and random noise, the robot motion command based on the GPS data suffers from sudden and unexpected change, resulting in jerky robot motion. The PF reduces corruption on the GPS data and prevents unexpected location error. The proposed method is used for navigation of a mobile robot in the 2011 Robot Outdoor Navigation Competition, which was held at Gwangju on the 16-th August 2011. The method restricted the robot location error below 0.5m along the navigation of 300m length.
Keywords
autonomous navigation; localization; IMU; GPS; MCL;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Defense Advanced Research Projects Agency (DARPA), DARPA GrandChallenge, Online source: http://www.darpa.mil/grandchallenge.
2 윤강섭, "초음파위성 시스템을 위한 개선된 위치추정 알고리즘", 한국전자통신학회논문지, 6 권, 5호, pp. 775-781, 2011.
3 Sebastian Thrun, Wolfram Burgard, and Dieter Fox, Probabilistic Robotics, The MIT Press, Cambridge, 2005.
4 S. Kim, C. Roh, S. Kang, and M. Park, "Outdoor navigation of a mobile robot using differential gps and curb detection," Proceedings of IEEE Internation Conference on Robotics and Automation, 2007.
5 노성우, 김태균, 고낙용, "GPS센서와 MCL 알고리즘을 이용한 실외환경에서의 이동로봇 위치추정" 한국지능시스템학회 2011년도 추계학술대회 학술발표논문집, 21권, 2호, pp. 49-51, 2011.
6 H. K. Lee, J. G. Lee, and G. I. Jee, ""Channelwise multipath detection for general GPS receivers,"" Journal of Control, Automation, and Systems Engineering (in Korean), Vol. 8, No. 9, pp. 818-826, 2002.
7 S. Thrun, "Learning metric-topological maps for indoor mobile robot navigation," Artificial Intelligence, Vol. 99, No. 1, pp. 21-71, 1998.
8 Borenstein, J,. Everett, B,.and Feng, L., "Where am I? Sensors and Methods for Mobile Robot Positioning." pp 130-131, 1996.
9 국립지리원, "지형․지적정보의연계활용연구", 1998.
10 Braasch, M.S., Fink, AM, "Improved Modeling of GPS Selective Availability", Proceeding ION GPS National Technical Meeting, San Francisco, pp. 121-130, 1993.
11 Goodchild, M.F., Introduction to GIS, National Center for Geographic Information and Analysis, Univ. of California, 1991.
12 Liddle, D.A., "Orthometric height determination by GPS" , Surveing and Mapping, Vol. 49, No. 1, pp. 5-16, 1989.
13 김용일, 박경환, "우리나라 국가기본도의 편차도표에 관한 연구", 한국GIS학회, Vol. 3, No. 1, pp. 139-149, 1996.
14 V. Zavorotny, A. Voronovich, "Scattering of GPS signals from the ocean with wind remote sensing application, IEEE Trans Geosci. Remote Sens., Vol. 38, No. 2, pp. 951-964, March, 2000.
15 김태균, 고낙용, 노성우, 이영필, "몬테 카를로 위치추정 알고리즘을 이용한 수중로봇의 위치추정", 한국전자통신학회논문지, 6권, 2호, pp. 288-295, 2011.
16 문용선, 배영철, 노상현, 조광훈, 박용구, "이동 로봇 모듈의 RTC 미들웨어 개발", 한국전자통신학회논문지, 5권, 2호, pp. 214-220, 2010.