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.
Abstract (translated)
感知任务在多个应用领域中开发自动操作和系统具有关键作用。在铁路运输领域，这些任务可以提高包括列车定位、信号识别和轨道区分的各种操作的安全性、可靠性和效率。然而，为了测试这些新颖算法，收集大量准确标注的 dataset 在铁路环境中具有极具挑战性的，因为铁路环境中访问基础设施受到严重限制，并且与正确装备列车所需的传感器（如摄像头和 LiDAR）相关的实际困难。图形引擎工具的显著创新提供了用图形模拟创建现实合成数据集的新方法。为了说明在铁路领域使用图形模拟进行早期阶段感知任务测试的优势，本文对在虚拟合成环境和真实世界场景中应用 SLAM 算法的性能进行了比较分析。分析依赖于使用 Unreal Engine 最新版本创建的虚拟铁路环境，数据收集得以进行，并允许研究具有挑战性的场景，包括低可见度、危险操作模式和复杂环境。结果强调了图形模拟在铁路领域提高感知任务的可行性和潜力。
URL
https://arxiv.org/abs/2403.17084
