A Monte-Carlo Based Stochastic Approach of Soccer Robot Self-Localization

被引：0
|
作者：
Li, Wei [1 ]
Zhao, Yannan [1 ]
Song, Yixu [1 ]
Yang, Zehong [1 ]
机构：
[1] Tsinghua Univ, Beijing 100084, Peoples R China
来源：
2008 CONFERENCE ON HUMAN SYSTEM INTERACTIONS, VOLS 1 AND 2 | 2008年
关键词：
Monte-Carlo localization; Self localization; Soccer robot;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The self-localization problem of mobile robot is considered as one of the most difficult problems in robotics, and is generally handled through stochastic methods. This paper discusses a stochastic approach of soccer robot self-localization using Monte-Carlo Localization (MCL) method. In MCL, environment information of lines, goals, balls, etc. is first retrieved and processed; such information is used to deal with state uncertainty of robot self-localization. Experiments show that MCL is a fast and robust way in discovering position and pose of soccer robot.
引用
收藏
页码：921 / 926
页数：6
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