Underground Pipeline Mapping Based on Dirichlet Process Mixture Model

Abstract

Underground pipeline mapping is important in urban construction. There are few specific procedures and approaches to map underground pipelines using ground penetration radar (GPR) without knowing the number of buried pipelines. In this paper, an automatic pipeline mapping model, the Dirichlet Process Pipeline Mapping Model (DPPMM), is introduced with GPR and Global Position System (GPS) data as input. By combining the GPR and GPS the position, direction, depth and size of pipelines could be estimated. The number of buried pipelines in the detection site could be automatically estimated with the benefit of DPPMM, without any prior knowledge. By adopting this model, the probabilities of each survey point belonging to each pipeline are calculated, and the pipeline directions and locations are also estimated. The experimental results demonstrate that this model could obtain more accurate pipeline maps than other state-ofthe-art algorithms in various experimental settings.

Keywords

• Ground penetrating radar (GPR)
• pipeline mapping
• clustering
• nonparametric Bayesian model