Abstract
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 underground railway scenarios is viable, different VO architectures are selected. For that, the state of the art of deep learning based VO approaches is analyzed. Four categories can be defined in the VO approaches defined in the last few years: (1) supervised pure deep learning based solutions; (2) solutions combining geometric features and deep learning; (3) solutions combining inertial sensors and deep learning; and (4) unsupervised deep learning solutions. A dataset composed of underground train stop operations was also created, where the ground truth is labeled according to the onboard unit SIL-4 ERTMS/ETCS odometry data. The dataset was recorded by using a camera installed in front of the train. Preliminary experimental results demonstrate that deep learning based VO solutions are applicable in underground train stop operations.
Original language | English |
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Title of host publication | Fifteenth International Conference on Quality Control by Artificial Vision |
Editors | Kenji Terada, Akio Nakamura, Takashi Komuro, Tsuyoshi Shimizu |
Publisher | SPIE - International Society for Optical Engineering |
Publication date | 2021 |
Article number | 1179402 |
ISBN (Electronic) | 9781510644267 |
DOIs | |
Publication status | Published - 2021 |
Event | 15th International Conference on Quality Control by Artificial Vision - Tokushima, Virtual, Japan Duration: 12 May 2021 → 14 May 2021 |
Conference
Conference | 15th International Conference on Quality Control by Artificial Vision |
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Country/Territory | Japan |
City | Tokushima, Virtual |
Period | 12/05/2021 → 14/05/2021 |
Sponsor | Technical Committee on Industrial Application of Image Processing, Japan Society for Precision Engineering (JSPE) |
Series | Proceedings of SPIE - The International Society for Optical Engineering |
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Volume | 11794 |
ISSN | 0277-786X |
Bibliographical note
Funding Information: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.
Publisher Copyright:
© 2021 SPIE.
Keywords
- Artificial intelligence
- Autonomous train
- Computer vision
- Deep learning
- Rail transportation