Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

Abstract

Digitalisation in railway networks harnesses digital technologies to optimise operations, leading to enhanced efficiency, and reduced energy consumption. By analysing real-time data, railways can predict maintenance needs, improve passenger experiences, and seamlessly integrate with other transport modes. As societies strive for sustainable transportation solutions, it is imperative to understand and collect digitalisation techniques to enhance efficiency and reduce the ecological footprint of railway networks. This paper serves as a snapshot of the current state of the art addressing the pivotal role of point cloud techniques in advancing railway digitalisation and providing valuable pointers for future research directions. Employing a systematic review approach, our study concentrates exclusively on research centred around railway assets and their digitalisation via point cloud data. We have themed the literature into pre-processing, modelling, and digital twinning. Within this review, we analyse diverse modelling and pre-processing techniques and categorise them for clarity. The digital twin techniques are also collected, though these techniques are scarce in the context of railway infrastructure and point clouds. The paper also presents a compilation of dataset statistics highlighting the scarcity of openly available railway-specific datasets. This scarcity considerably hampers the feasibility of research reproducibility and the comparative analysis of different approaches. Our conclusion reflects on the challenges encountered and proposes a course for future research. Particularly, we conclude that hybrid methodologies that combine machine learning with structure-based techniques hold substantial promise toward creating digital twins, considering the intrinsic characteristics of railway infrastructure.

Keywords

• Railways
• point clouds
• digitalization
• infrastructure
• deep learning
• digital twin