Using images to avoid collisions and bypass obstacles in indoor environments

  • David Silva de Medeiros IFCE
  • Thiago Henrique Araújo IFCE
  • Elias Teodoro da Silva Júnior IFCE
  • Geraldo Luis Bezerra Ramalho IFCE

Resumo


Convolutional Neural Network (CNN) has contributed a lot to the advancement of autonomous navigation techniques, and such systems can be adapted to facilitate the movement of robots and visually impaired people. This work presents an approach that uses images to avoid collisions and bypass obstacles in indoor environments. The constructed dataset uses information from forward and lateral speeds during walks to determine collisions and obstacle avoidance. VGG16, ResNet50, and Dronet architectures were used to evaluate the dataset. Finally, reflections on the dataset characteristics are added, and the CNNs performance is presented.

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Publicado
18/10/2021
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MEDEIROS, David Silva de; ARAÚJO, Thiago Henrique; SILVA JÚNIOR, Elias Teodoro da; RAMALHO, Geraldo Luis Bezerra. Using images to avoid collisions and bypass obstacles in indoor environments. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 158-161. DOI: https://doi.org/10.5753/sibgrapi.est.2021.20030.