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.
Highlights
Underground pipeline mapping is an important part in the urban construction to avoid inaccurate excavations during pipes maintenance and rehabilitation
The Dirichlet Process Pipeline Mapping Model (DPPMM) is proposed to map the buried pipelines from Ground Penetrating Radar (GPR) and Global Position System (GPS) data. It is based on a nonparametric Bayesian model, i.e. the Dirichlet Process Mixture Model (DPMM)
The probability model, Dirichlet Process Mixture Model, and how it works with the survey points are introduced
Summary
Underground pipeline mapping is an important part in the urban construction to avoid inaccurate excavations during pipes maintenance and rehabilitation. The Dirichlet Process Pipeline Mapping Model (DPPMM) is proposed to map the buried pipelines from GPR and GPS data. It is based on a nonparametric Bayesian model, i.e. the Dirichlet Process Mixture Model (DPMM). Kobayashi and Nakano [20] proposed a GPR signal processing algorithm based on DPMM It could detect and fit the hyperbolas. The main contributions of this paper are summarized as below: 1) As a nonparametric model, some parameters, such as the number of pipelines, could be automatically estimated during mapping. 3) By randomly sampling survey points from the dataset, this model could simulate the base distribution without any prior knowledge
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