Detalhes bibliográficos
| Resumo: | Autonomous mobile robots applications require a robust navigation system, which ensures the proper movement of the robot while performing their tasks. The key challenge in the navigation system is related to the indoor localization. Simultaneous Localization and Mapping (SLAM) techniques combined with Adaptive Monte Carlo Localization (AMCL) are widely used to localize robots. However, this approach is susceptible to errors, especially in dynamic environments and in presence of obstacles and objects. This paper presents an approach to improve the estimation of the indoor pose of a wheeled mobile robot in an environment. To this end, the proposed localization system integrates the AMCL algorithm with the position updates and corrections based on the artificial vision detection of fiducial markers scattered throughout the environment to reduce the errors accumulated by the AMCL position estimation. The proposed approach is based on Robot Operating System (ROS), and tested and validated in a simulation environment. As a result, an improvement in the trajectory performed by the robot was identified using the SLAM system combined with traditional AMCL corrected with the detection, by artificial vision, of fiducial markers. |
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| Autores principais: | Oliveira Júnior, Alexandre de |
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| Outros Autores: | Piardi, Luis; Bertogna, Eduardo Giometti; Leitão, Paulo |
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| Assunto: | Computer vision Indoor positioning systems |
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| Ano: | 2021 |
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| País: | Portugal |
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| Tipo de documento: | comunicação em conferência |
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| Tipo de acesso: | acesso aberto |
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| Instituição associada: | Instituto Politécnico de Bragança |
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| Idioma: | inglês |
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| Origem: | Biblioteca Digital do IPB |
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