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Abstract

Geographic trajectory of a pipeline is important information for pipeline maintenance and leak detection. Although accurate trajectory of a ground pipeline usually can be directly measured by using global positioning system technology, it is much difficult to determine trajectory for an underground pipeline where global positioning system signal cannot be received. In this paper, a new method to determine trajectory for an underground pipeline by using a pipeline inspection robot is proposed. The robot is equipped with a low-cost inertial measurement unit and odometers. The kinematic model, measurement model and error propagation model are established for estimating position, velocity and attitude of the robot. The path reconstruction algorithm for the robot is proposed to improve accuracy of trajectory determination based on pipeline features. The experiment is given to illustrate that the position errors of the proposed method are less than 40% of that of the standard extended Kalman filter.
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Bibliography

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Authors and Affiliations

Shuo Zhang
1
Stevan Dubljevic
1

  1. University of Alberta, Department of Chemical & Materials Engineering, T6G 2R3 Edmonton, AB, Canada
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Abstract

Conventionally, the filtering technique for attitude estimation is performed using gyros or attitude dynamics

models. In order to extend the application range of an attitude filter, this paper proposes a quaternionbased

filtering framework for gyroless attitude estimation without an attitude dynamics model. The attitude

estimation system is established based on a quaternion kinematic equation and vector observation models.

The angular velocity in the system is determined through observation vectors from attitude sensors and the

statistical properties of the angular velocity error are analysed. A Kalman filter is applied to estimate the

attitude error such that the effect from the angular velocity error is compensated with its statistical properties

at each sampling moment. A numerical simulation example is presented to illustrate the performance of the

proposed algorithm.

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Authors and Affiliations

Shuo Zhang
Fei Xing
Ting Sun
Zheng You

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