Deep-Learning-Based Visual Odometry Models for Mobile Robotics

  • Frederico Luiz Martins de Sousa UFOP
  • Natália F. de C. Meira UFOP
  • Ricardo Augusto Rabelo Oliveira UFOP
  • Mateus Coelho Silva UFOP


Odometry is a common problem in navigation systems where there is a need to estimate the position of the vehicle or carrier in the environment. To perform autonomous tasks, robotic or intelligent devices need to be aware of their position in the environment. There are many strategies to solve an odometry problem. This work explores a visual odometry solution with a deep neural network to infer the robotic vehicle's position in a known and mapped environment. The first robot, equipped with a LIDAR, IMU, and camera, maps the environment through a SLAM technique to perform this task. The data gathered by this first robot is used as ground truth to train the neural network, and later, other robots with only one camera can locate themselves in the environment. We also propose a validation and evaluation of the neural network.
Palavras-chave: mobile robotics, odometry, edge-computing, deep neural networks, Robot Operating System


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SOUSA, Frederico Luiz Martins de; MEIRA, Natália F. de C.; OLIVEIRA, Ricardo Augusto Rabelo; SILVA, Mateus Coelho. Deep-Learning-Based Visual Odometry Models for Mobile Robotics. In: TRABALHOS EM ANDAMENTO - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 11. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 128-133. ISSN 2763-9002. DOI: