An Efficient Monte Carlo Method for Mobile Robot Localization
Eryong Wu, Zhiyu Xiang, Jilin Liu · 2006
Traditional Kalman filter or extended Kalman filter has been used broadly for mobile robot localization. However in some circumstances, its prior Gaussian hypothesis becomes unacceptable and limits the localization precision. For this reason, Monte Carlo method is used for the robot localization. In this paper, the algorithm implementation is advanced after introducing the basic theory of Monte Carlo method. According to the characteristics of robot movement, a new resample method is presented based on adapting the sample size and their particles space distribution. Finally experiments confirm the advantage of boosting accuracy and convergence speed about this idea