Navigation Kalman Filter Design for Pipeline Pigging

Abstract

The paper discusses the development of an adaptive Kalman filter for pipeline surveying applications. Various measurement models are developed and numerically tested using a real natural gas pipeline dataset. Since, for tactical-grade IMUs, odometer-derived velocity measurements alone cannot yield good results, two non-holonomic constraints are augmented to make the three-direction measurement model. Smoothing computation is also applied to yield the best trajectory using all the information contained in the past, current and future measurements. The trajectory errors are within 10–20 m for extreme cases, and within 10 m for most cases. RMS of the trajectory errors is expected to be about 2·5 m.