Zerrendatu honen arabera: egilea "Etxeberria Garcia, Mikel"
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Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation
Arana-Arexolaleiba, Nestor (IEEE, 2020)The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ... -
Image Enhancement using GANs for Monocular Visual Odometry
Zubieta Ansorregi, Jon; Etxeberria Garcia, Mikel; Zamalloa, Maider; Arana-Arexolaleiba, Nestor (IEEE, 2021)Drones, mobile robots, and autonomous vehicles use Visual Odometry (VO) to move around complex environments. ORB-SLAM or deep learning-based approaches like DF-VO are two of the state-of-the-art technics for monocular VO. ... -
Monocular visual odometry for underground railway scenarios
Etxeberria Garcia, Mikel; Labayen, Mikel; Eizaguirre, Fernando; Zamalloa, Maider; Arana-Arexolaleiba, Nestor (SPIE, 2021)In this paper, the application of monocular Visual Odometry (VO) solutions for underground train stopping operation are explored. In order to analyze if the application of monocular VO solutions in challenging environments as ... -
Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case
Arana-Arexolaleiba, Nestor (IEEE, 2022)Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the ...
