Real-Time Elbow and T-Junction Detection and Estimation Framework for In-Line Inspection in Natural Gas Pipelines Using 3D Point Cloud

In-line inspection (ILI) is essential for maintaining the integrity of natural gas pipelines. In ILI, pipeline inspection gauges (PIGs) autonomously navigate through pipelines under real-time and resource-constrained conditions to measure pipeline irregularities. Elbow and T-junction detection and estimation (ETDE) play a critical role in enabling PIG navigation to avoid speed excursions which prevent effective data collection or even damage the PIG and pipelines. Existing approaches are often computationally intensive, incomplete, or late estimations, making them unsuitable for real-time operation in such environments. This paper proposes a real-time elbow and T-junction detection and estimation (RT-ETDE) framework that efficiently detects elbows and T-junctions and estimates their critical parameters for PIG navigation using 3D point clouds. Our framework relies on intelligent point cloud partition and feature extraction together with simple geometric solutions which greatly reduce computational complexity. Furthermore, several real-time adaptation techniques are introduced to ensure real-time operation. Experimental results on a prototype pipeline demonstrate that the RT-ETDE framework consistently detects elbows and T-junctions while achieving a 10 Hz frame rate on a real PIG processing unit, validating its suitability for next-generation pipeline inspection systems.