Comparing Vision-based Monte-Carlo Localization Methods

Autor(es)

Almeida, Aislan C. ; Neto, Sylvio R. J. ; Bianchi, Reinaldo A. C. ; DoNascimento, TP ; Colombini, EL ; DeBrito, AV ; Garcia, LTD ; Sa, STD ; Goncalves, LMG

• Tipo de documento: Artigo Científico
• Fonte: 15TH LATIN AMERICAN ROBOTICS SYMPOSIUM 6TH BRAZILIAN ROBOTICS SYMPOSIUM 9TH WORKSHOP ON ROBOTICS IN EDUCATION (LARS/SBR/WRE 2018); v. N/A, p. 6-pg., 2018-01-01.

Resumo

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. (AU)

• Processo FAPESP: 16/21047-3 - ALIS: Aprendizado Autônomo em Sistemas Inteligentes
• Beneficiário: Anna Helena Reali Costa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 16/18792-9 - Descrição, representação e solução de jogos espaciais
• Beneficiário: Paulo Eduardo Santos
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE