Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case

Etxeberria-Garcia, Mikel; Zamalloa, Maider; Arana-Arexolaleiba, Nestor; Labayen, Mikel

doi:10.1109/access.2022.3187209

Cited by 4 publications

(1 citation statement)

References 85 publications

(89 reference statements)

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“…In order to run the tests, part of a private CAF dataset [29] containing stereo images of an urban railway environment has been used. The dataset contains 19 sequences on the railway track.…”

Section: Test Descriptionmentioning

confidence: 99%

HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence

et al. 2023

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The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources.

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Section: Test Descriptionmentioning

confidence: 99%

HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence

et al. 2023

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Vision-aided precise positioning for long-reach robotic manipulators using local calibration

Mäkinen,

Mustalahti,

Launis

et al. 2023

Advanced Robotics

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Experimental Evaluation of Monocular Visual–Inertial SLAM Methods for Freight Railways

Li,

Lou,

Song

et al. 2023

IEEE Sensors J.

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Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case

Abstract: This work was supported in part by the Basque Government through BIKAINTEK2018 program and CAF Signalling. In collaboration between Ikerlan, CAF Signalling and Mondragon Unibertsitatea. We would also like to thank Euskotren and Eusko Trenbide Sarea.

Cited by 4 publications

References 85 publications

HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence

HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence

Vision-aided precise positioning for long-reach robotic manipulators using local calibration

Experimental Evaluation of Monocular Visual–Inertial SLAM Methods for Freight Railways

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