  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.
