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

页码：921 / 926

页数：6

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