No abstract

Classical Visual Simultaneous Localization and Mapping (VSLAM) algorithms can be easily induced to fail when either the robot's motion or the environment is too challenging. The use of Deep Neural Networks to enhance VSLAM algorithms has recently achieved promising results, which we call hybrid methods. In this paper, we compare the performance of hybrid monocular VSLAM methods with different learned feature descriptors. To this end, we propose a set of experiments to evaluate the robustness of the algorithms under different environments, camera motion, and camera sensor noise. Experiments conducted on KITTI and Euroc MAV datasets confirm that learned feature descriptors can create more robust VSLAM systems.

The Simultaneous Localization and Mapping (SLAM) problem addresses the possibility of a robot to localize itself in an unknown environment and simultaneously build a consistent map of this environment. Recently, cameras have been successfully used to get the environment’s features to perform SLAM, which is referred to as visual SLAM (VSLAM). However, classical VSLAM algorithms can be easily induced to fail when the robot motion or the environment is too challenging. Although new approaches based on Deep Neural Networks (DNNs) have achieved promising results in VSLAM, they still are unable to outperform traditional methods. To leverage the robustness of deep learning to enhance traditional VSLAM systems, we propose to combine the potential of deep learning-based feature descriptors with the traditional geometry-based VSLAM, building a new VSLAM system called LIFT-SLAM. Experiments conducted on KITTI and Euroc datasets show that deep learning can be used to improve the performance of traditional VSLAM systems, as the proposed approach was able to achieve results comparable to the state-of-the-art while being robust to sensorial noise. We enhance the proposed VSLAM pipeline by avoiding parameter tuning for specific datasets with an adaptive approach while evaluating how transfer learning can affect the quality of the features extracted.

(HCU-UFU) é um setor muito importante para o tratamento de pacientes em estados críticos que necessitam de um monitoramento contínuo de seus sinais vitais. Porém, com o processo de admissão de paciente na UTI sendo em documento físico, muito tempo dos profissionais é redirecionado para processos burocráticos em si, e não para com a assistência à saúde. O objetivo desse projeto foi a concepção de um software para automatizar esse processo. Assim o software foi desenvolvido no setor de Tecnologia de Informação do HCU-UFU. Os resultados obtidos foram as interfaces de controle de pacientes da UTI-Adulto e a otimização da geração de relatórios. Palavras-chave: HCU-UFU, UTI-Adulto, software, admissão de paciente.