Vision-based Monte-Carlo localization for humanoid soccer robots
N/D
Tipo de produção
Artigo de evento
Data de publicação
2017-11-17
Texto completo (DOI)
Periódico
Proceedings - 2017 LARS 14th Latin American Robotics Symposium and 2017 5th SBR Brazilian Symposium on Robotics, LARS-SBR 2017 - Part of the Robotics Conference 2017
Editor
Texto completo na Scopus
Citações na Scopus
3
Autores
ALMEIDA, A. C.
COSTA, A. H. R.
Reinaldo Bianchi
Orientadores
Resumo
© 2017 IEEE.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.
Citação
ALMEIDA, A. C.; COSTA, A. H. R.; BIANCHI, R. Vision-based Monte-Carlo localization for humanoid soccer robots. Proceedings - 2017 LARS 14th Latin American Robotics Symposium and 2017 5th SBR Brazilian Symposium on Robotics, LARS-SBR 2017 - Part of the Robotics Conference 2017, p. 1-6, Nov. 2017.
Palavras-chave
Keywords
Assuntos Scopus
Humanoid robot; Humanoid soccer robots; Localization problems; Monte Carlo localization; Observation model; Self localization; Sensory input; Simulated experiments