Indoor magnetic fields are a combination of Earth's magnetic field and disruptions induced by ferromagnetic objects, such as steel structural components in buildings. As a result of these disruptions, pervasive in indoor spaces, magnetic field data is often omitted from navigation algorithms in indoor environments. This paper leverages the spatially-varying disruptions to Earth's magnetic field to extract positional information for use in indoor navigation algorithms. The algorithm uses a rate gyro and an array of four magnetometers to estimate the robot's pose. Additionally, the magnetometer array is used to compute attitude-invariant measurements associated with the magnetic field and its gradient. These measurements are used to detect loop closure points. Experimental results indicate that the proposed approach can estimate the pose of a ground robot in an indoor environment within meter accuracy.

翻译：摘要：室内磁场是地磁场与铁磁性物体（如建筑物中的钢结构构件）引起的磁场畸变共同作用的结果。由于这些畸变在室内空间中普遍存在，磁场数据常常被排除在室内导航算法之外。本文利用地磁场中因空间变化而产生的畸变来提取位置信息，并将其应用于室内导航算法。该算法使用速率陀螺仪和由四个磁强计组成的阵列来估计机器人的位姿。此外，磁强计阵列还被用于计算与磁场及其梯度相关的姿态不变测量值。这些测量值用于检测回环闭合点。实验结果表明，所提出的方法能够在室内环境中以米级精度估计地面机器人的位姿。