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Indoor magnetic fields are a combination of Earth's magnetic field anddisruptions induced by ferromagnetic objects, such as steel structuralcomponents in buildings. As a result of these disruptions, pervasive in indoorspaces, magnetic field data is often omitted from navigation algorithms inindoor environments. This paper leverages the spatially-varying disruptions toEarth's magnetic field to extract positional information for use in indoornavigation algorithms. The algorithm uses a rate gyro and an array of fourmagnetometers to estimate the robot's pose. Additionally, the magnetometerarray is used to compute attitude-invariant measurements associated with themagnetic field and its gradient. These measurements are used to detect loopclosure points. Experimental results indicate that the proposed approach canestimate the pose of a ground robot in an indoor environment within meteraccuracy.

Magnetic Navigation using Attitude-Invariant Magnetic Field Information for Loop Closure Detection