Underground Pipeline Mapping From Multipositional Data: Data Acquisition Platform and Pipeline Mapping Model.

Maintaining and upgrading underground pipelines are major undertakings in urban operations, where accurately locating buried pipelines has long been an issue. In this article, we propose a pipeline mapping method based on integrating multipositional pipeline data, which includes the multisensor data acquisition (MDA) platform and the scalable probability-based pipeline mapping (SP-PM) model. To effectively collect pipeline data at multiple positions, several pipeline detecting and positioning sensors are equipped in the MDA platform. Different types of sensor data are synchronously collected and processed to obtain manageable pipeline data at multiple positions within the detected area, such as the radius, depth, and positioned points of underground pipelines. The SP-PM model is then proposed, where the obtained pipeline data are probabilistically described and classified into classes. Each class contains the pipeline data possibly generated by the same pipeline. The classified data are then iteratively integrated to estimate the pipeline map with the maximum probability. The SP-PM model probabilistically estimates the degree of correlation between the multipositional pipeline data and each potential pipeline, and it has no strict requirement on existing statutory records or limits on the number of detections per pipeline. Unrecorded pipelines could be identified and involved into the generated pipeline map, along with continuous adjusting of the pipelines' number. We conducted experiments on real-world environments. The experimental results verify the accuracy and efficiency of the proposed method for buried pipeline mapping.