Automatic Point Cloud Semantic Segmentation of Complex Railway Environments

Abstract

The growing development of data digitalisation methods has increased their demand and applications in the transportation infrastructure field. Currently, mobile mapping systems (MMSs) are one of the most popular technologies for the acquisition of infrastructure data, with three-dimensional (3D) point clouds as their main product. In this work, a heuristic-based workflow for semantic segmentation of complex railway environments is presented, in which their most relevant elements are classified, namely, rails, masts, wiring, droppers, traffic lights, and signals. This method takes advantage of existing methodologies in the field for point cloud processing and segmentation, taking into account the geometry and spatial context of each classified element in the railway environment. This method is applied to a 90-kilometre-long railway lane and validated against a manual reference on random sections of the case study data. The results are presented and discussed at the object level, differentiating the type of the element. The indicators F1 scores obtained for each element are superior to 85%, being higher than 99% in rails, the most significant element of the infrastructure. These metrics showcase the quality of the algorithm, which proves that this method is efficient for the classification of long and variable railway sections, and for the assisted labelling of point cloud data for future applications based on training supervised learning models.

Railway Lidar Semantic Segmentation With Axially Symmetrical Convolutional Learning

“…In our specific industrial context, traditional model-based approaches are often object oriented and rely on userimplemented geometrical global and local features, trajectory information -e.g. Lamas et al (2021). Although these approaches perform well in controlled environments, they need to be redesigned for any change in use case and are sensitive to unforeseen characteristics.…”

3D POINT CLOUD TO BIM: AUTOMATED APPLICATION TO DEFINE IFC ALIGNMENT AND ROADWAY WIDTH ENTITIES FROM MLS-ACQUIRED LiDAR DATA OF MOUNTAIN ROADS

“…This process is described in Figure 6. To do so, the first phase is to apply a Modified Principal Component Analysis (MPCA) described in (Lamas et al, 2021).…”

“…This section describes the process of calculating the direction perpendicular to the trajectory in the plane of the roadway. The process consists of applying MPCA (Lamas et al, 2021) to each point and its nd nearest points. If possible, the points considered are half in front and half behind the selected trajectory point.…”

iBALR3D: imBalanced-Aware Long-Range 3D Semantic Segmentation

“…Notably, Javier Grandio et al [3] developed a multi-modal method for railway infrastructure point clouds, focusing on panoptic segmentation of linear and pole-like objects. Daniela Lorena Lamas et al [4] introduced an innovative algorithm that leverages geometry and spatial context, enhancing segmentation in railway environments (e.g., rails, masts, wiring, droppers, traffic lights, and signals). Additionally, Jingru Wang et al [5] proposed a robust method for segmenting point cloud data of communication towers and accessory equipment based on geometrical shape context from a 3D point cloud.…”