ROBOT LOCALIZATION FROM SURROUNDING VIEWS USING MONTE CARLO ROBUST AGAINST SUCCESSIVE OUTLIERS

被引：0

作者：

Nakajima, Shigeyoshi ^{[1
]}

Ikejiri, Masataka ^{[2
]}

Toriu, Takashi ^{[1
]}

机构：

[1] Osaka City Univ, Grad Sch Engn, Sumiyoshi Ku, Osaka 5588585, Japan

[2] Sharp Co Ltd, Tenri, Nara 6328567, Japan

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2011年 / 7卷 / 7A期

关键词：

Autonomous robot; Localization; Monte Carlo; Clustering; Dead reckoning;

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：

We propose clustering Monte Carlo as a new method of localization for an autonomous robot from surrounding views and dead reckoning data. Localization is one of very important techniques for autonomous robots in many scenes, e.g., RoboCup (autonomous robot soccer league). Recently, a resetting Monte Carlo localization method was proposed. However, the method cannot deal with successive outliers well. The proposed methods are improvements of the resetting Monte Carlo localization method and good at dealing with successive outliers.

引用

页码：3869 / 3880

页数：12

共 23 条

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