A Comparative Analysis of Visual Odometry in Virtual and Real-World Railways Environments

TL;DR

This paper compares the performance of a visual odometry SLAM algorithm in virtual railway environments created with Unreal Engine and real-world settings, demonstrating the potential of synthetic data for railway perception tasks.

Contribution

It provides a novel comparative analysis showing how synthetic environments can effectively support perception algorithm testing in railway applications.

Findings

Synthetic environments enable testing of perception tasks in challenging scenarios.

SLAM algorithm performance in virtual environments closely matches real-world results.

Graphic simulation proves useful for early-stage development in railway perception systems.

Abstract

Perception tasks play a crucial role in the development of automated operations and systems across multiple application fields. In the railway transportation domain, these tasks can improve the safety, reliability, and efficiency of various perations, including train localization, signal recognition, and track discrimination. However, collecting considerable and precisely labeled datasets for testing such novel algorithms poses extreme challenges in the railway environment due to the severe restrictions in accessing the infrastructures and the practical difficulties associated with properly equipping trains with the required sensors, such as cameras and LiDARs. The remarkable innovations of graphic engine tools offer new solutions to craft realistic synthetic datasets. To illustrate the advantages of employing graphic simulation for early-stage testing of perception tasks in the railway domain, this paper presents a comparative analysis of the performance of a SLAM algorithm applied both in a virtual synthetic environment and a real-world scenario. The analysis leverages virtual railway environments created with the latest version of Unreal Engine, facilitating data collection and allowing the examination of challenging scenarios, including low-visibility, dangerous operational modes, and complex environments. The results highlight the feasibility and potentiality of graphic simulation to advance perception tasks in the railway domain.