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.

翻译：感知任务在多个应用领域的自动化运行和系统开发中扮演着关键角色。在铁路运输领域，这些任务能够提升列车定位、信号识别和轨道判别等操作的安性、可靠性与效率。然而，由于进入基础设施受到严格限制，以及为列车配备摄像头和激光雷达等必要传感器存在实际困难，在铁路环境中收集大量且精确标注的数据集以测试此类新型算法面临极大挑战。图形引擎技术的显著创新为制作逼真的合成数据集提供了新方案。为论证在铁路领域感知任务的早期测试中采用图形模拟的优势，本文针对应用于虚拟合成环境与现实场景中的同步定位与地图构建（SLAM）算法的性能进行了比较分析。该分析利用最新版虚幻引擎创建的虚拟铁路环境，促进了数据收集，并支持对低能见度、危险运行模式和复杂环境等具有挑战性的场景进行检验。结果凸显了图形模拟在推动铁路领域感知任务发展方面的可行性与潜力。