In this brief, a high-accuracy inertial navigation system (INS) for a pipeline inspection gauge (PIG) is proposed. Two mechanization approaches are investigated. First, a full INS dynamic model is used, and second, a 3-D reduced inertial sensor system (RISS) is utilized. The INS uses the full inertial measurement unit (IMU) data to calculate the navigation solution, whereas the RISS uses an encoder, one single-axis gyroscope, and two accelerometers. The RISS model is proposed in this brief for its better accuracy and less computational complexity. Due to the accumulated error in the INS or RISS solution, an extended Kalman filter is proposed to fuse the IMU data with PIG measurements. For the full INS model, these measurements are the encoder’s derived velocity constraint and the detected pipe length measurement. On the other hand, for the RISS model, it only refers to the detected pipe length measurement. An experimental setup, with a prototype of the in-pipe robot, is designed and built to test and validate our algorithms in a real pipe environment. Subsequently, the accuracy of the proposed algorithms is verified experimentally.

中文翻译：

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管道检测仪惯性导航系统

在此简介中，提出了一种用于管道检查规（PIG）的高精度惯性导航系统（INS）。研究了两种机械化方法。首先，使用完整的INS动态模型，其次，使用3-D减小惯性传感器系统（RISS）。INS使用完整的惯性测量单位（IMU）数据来计算导航解决方案，而RISS使用编码器，一个单轴陀螺仪和两个加速度计。本摘要中提出了RISS模型，因为它具有更好的准确性和更少的计算复杂性。由于INS或RISS解决方案中的累积误差，建议使用扩展的卡尔曼滤波器将IMU数据与PIG测量融合。对于完整的INS模型，这些测量值是编码器得出的速度约束和检测到的管道长度测量值。另一方面，对于RISS模型，它仅指检测到的管道长度测量值。设计并构建了带有管道内机器人原型的实验装置，以在真实的管道环境中测试和验证我们的算法。随后，通过实验验证了所提算法的准确性。