Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.
Original languageEnglish
Number of pages14
Publication statusPublished - 3 Feb 2024
EventOptimization, Learning Algorithms and Applications - OL2A 2023 - Ponta Delgada, Portugal
Duration: 27 Sept 202329 Sept 2023


ConferenceOptimization, Learning Algorithms and Applications - OL2A 2023
Abbreviated titleOL2A 2023
CityPonta Delgada
Internet address


  • indoor localization
  • CNN
  • robotics competitions


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  • Best Paper Award - OL2A 2023

    Klein, Luan Carlos (Recipient), Mendes, João (Recipient), Braun, João (Recipient), Martins, Felipe (Recipient), Schneider de Oliveira, André (Recipient), Costa, Paulo Gomes (Recipient), Wörtche, Heinrich (Recipient) & Lima, José (Recipient), 29 Sept 2023

    Prize: Prize (including medals and awards)


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