Underground Pipeline Positioning Method Utilizing Dual Cascade Heading Correction Based on Sparse GNSS Reference Points

Underground pipeline networks, as critical urban lifelines, require advanced inspection technologies to address structural degradation and corrosion. External information sources are rare in complex pipeline scenarios and constructing them is both costly and difficult. Reference points leads to inadequate centerline positioning precision and makes it challenging to meet defect point detection criteria. This study creates an adaptive error compensation model to successfully reduce the effects of alignment errors and heading drift errors in inertial navigation systems by building a dual cascade heading correction architecture that incorporates multisource data from miniature inertial measurement units (IMUs), odometers, and sparse GNSS reference points (SGRPs). Our tests show that the proposed technique preserves meter-level relative errors. Compared to the first-stage correction process, it significantly improves the ability of positioning error suppression in areas with intermittent GNSS signals by 70%. By using heading correction, the technique maintains convergence of the positioning error. Its position accuracy is 0.2% of the distance traversed in a 160-min pipeline experiment. This technology will promote the use of inexpensive discrete point correction technology in intelligent pipeline networks and set the groundwork for developing a scalable technology paradigm for the Internet of Things (IoT) in urban subterranean positioning.