Methods for the Segmentation of Reticular Structures Using 3D LiDAR Data: A Comparative Evaluation

Reticular structures are the basis of major infrastructure projects, including bridges, electrical pylons and airports. However, inspecting and maintaining these structures is both expensive and hazardous, traditionally requiring human involvement. While some research has been conducted in this field of study, most efforts focus on faults identification through images or the design of robotic platforms, often neglecting the autonomous navigation of robots through the structure. This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures, thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments. The paper proposes multiple approaches for the binary segmentation between navigable surfaces and background from 3D point clouds captured from metallic trusses. Approaches can be classified into two paradigms: analytical algorithms and deep learning methods. Within the analytical approach, an ad hoc algorithm is developed for segmenting the structures, leveraging different techniques to evaluate the eigendecomposition of planar patches within the point cloud. In parallel, widely used and advanced deep learning models, including PointNet, PointNet++, MinkUNet34C, and PointTransformerV3, are trained and evaluated for the same task. A comparative analysis of these paradigms reveals some key insights. The analytical algorithm demonstrates easier parameter adjustment and comparable performance to that of the deep learning models, despite the latter's higher computational demands. Nevertheless, the deep learning models stand out in segmentation accuracy, with PointTransformerV3 achieving impressive results, such as a Mean Intersection Over Union (mIoU) of approximately 97%. This study highlights the potential of analytical and deep learning approaches to improve the autonomous navigation of climbing robots in complex truss structures. The findings underscore the trade-offs between computational efficiency and segmentation performance, offering valuable insights for future research and practical applications in autonomous infrastructure maintenance and inspection.