Monocular visual odometry for underground railway scenarios

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.