Publicação

A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition

Ver documento

Detalhes bibliográficos
Resumo:Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.
Autores principais:Klein, Luan C.
Outros Autores:Braun, João; Mendes, João; Pinto, Vítor H.; Martins, Felipe N.; Oliveira, Andre Schneider; Oliveira, Andre Schneider; Wörtche, Heinrich; Costa, Paulo Gomes da; Lima, José
Assunto:Indoor localization Machine learning Fiducial markers Industry 4.0 Robotics competitions
Ano:2023
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB

Registos relacionados

Registos relacionados