Comparing Vision-based Monte-Carlo Localization Methods

被引：1

作者：

Almeida, Aislan C. ^{[1
]}

Neto, Sylvio R. J. ^{[1
]}

Bianchi, Reinaldo A. C. ^{[1
]}

机构：

[1] Ctr Univ FEI, Elect Engn Dept, Sao Bernardo Do Campo, SP, Brazil

来源：

15TH LATIN AMERICAN ROBOTICS SYMPOSIUM 6TH BRAZILIAN ROBOTICS SYMPOSIUM 9TH WORKSHOP ON ROBOTICS IN EDUCATION (LARS/SBR/WRE 2018) | 2018年

基金：

巴西圣保罗研究基金会;

关键词：

PARTICLE FILTER;

D O I：

10.1109/LARS/SBR/WRE.2018.00084

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The task of localization is a recurrent subject in the domain of autonomous robotics. Even though it is a common subject, the differences among domains causes the need of different algorithms to solve the localization problem. This paper proposes a implementation of the Monte-Carlo Localization algorithm in order to solve the localization problem for the domain of the RoboCup Humanoid Soccer KidSize. It implements methods to change the quantity of particles in function of their dispersion, to solve the robot kidnapping problem by scattering the particles and to predict the observation that leads to improvement of the confidence in the position of the robot. The experiments show that the proposed methods improved the localization process in comparison with traditional methods regarding the execution time of the algorithm and the quality of the estimated position.

引用

页码：437 / 442

页数：6

共 24 条

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