Vision-based Monte-Carlo Localization for Humanoid Soccer Robots

被引：0
|
作者：
Almeida, Aislan C. [1 ]
Costa, Anna H. R. [2 ]
Bianchi, Reinaldo A. C. [1 ]
机构：
[1] Ctr Univ FEI, Elect Engn Dept, Sao Bernardo Do Campo, SP, Brazil
[2] Univ Sao Paulo, Escola Politen, Sao Paulo, SP, Brazil
来源：
2017 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS) AND 2017 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR) | 2017年
基金：
巴西圣保罗研究基金会;
关键词：
PARTICLE FILTER; SAMPLE-SIZE;
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：
In order to solve the self-localization problem, the Monte-Carlo Localization is proposed as a technique which is able to solve any localization problem. However, the implemented algorithm needs to be adapted to the robot and domain. Thus, this work presents a novel implementation for humanoid robots whose main sensory input is a camera, to be used in the domain of RoboCup Humanoid Soccer League. The paper proposes motion and observation models designed for the domain, and a method to determine the quantity of particles needed to represent the probability distribution. Finally, the proposals are validated by simulated experiments.
引用
收藏
页数：6
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