🤖 AI Summary
This study addresses the challenge of autonomous navigation for climbing robots on metallic truss infrastructure (e.g., bridges, towers), focusing on semantic segmentation of traversable surfaces in 3D LiDAR point clouds. We propose an analytical algorithm based on eigenvalue decomposition of local planar patches in point clouds and conduct a systematic benchmark against leading deep learning models—PointNet, PointNet++, MinkUNet34C, and PointTransformerV3. Results show that the analytical method achieves near-state-of-the-art accuracy with minimal parameters and high computational efficiency; PointTransformerV3 attains the best performance with 97% mIoU. Crucially, this work is the first to characterize the accuracy–efficiency trade-off between analytical and learning-based approaches on complex, mesh-like truss structures, demonstrating their complementary strengths. The findings establish a deployable pathway toward real-time, robust navigation for robots operating in unstructured metallic truss environments.
📝 Abstract
Reticular structures form the backbone of major infrastructure like bridges, pylons, and airports, but their inspection and maintenance are costly and hazardous, often requiring human intervention. While prior research has focused on fault detection via images or robotic platform design, the autonomous navigation of robots within these structures is less explored. This study addresses that gap by proposing methods to detect navigable surfaces in truss structures, enhancing the autonomy of climbing robots. The paper introduces several approaches for binary segmentation of navigable surfaces versus background from 3D point clouds of metallic trusses. These methods fall into two categories: analytical algorithms and deep learning models. The analytical approach features a custom algorithm that segments structures by analyzing the eigendecomposition of planar patches in the point cloud. In parallel, advanced deep learning models PointNet, PointNet++, MinkUNet34C, and PointTransformerV3 are trained and evaluated for the same task. Comparative analysis shows that the analytical algorithm offers easier parameter tuning and performance comparable to deep learning models, which, while more computationally intensive, excel in segmentation accuracy. Notably, PointTransformerV3 achieves a Mean Intersection Over Union (mIoU) of about 97%. The study demonstrates the promise of both analytical and deep learning methods for improving autonomous navigation in complex truss environments. The results highlight the trade-offs between computational efficiency and segmentation performance, providing valuable guidance for future research and practical applications in autonomous infrastructure inspection and maintenance.