Low-cost IMU sensors typically show significant amounts of drift and offset. To analyze data from such sensors, additional information is required to compensate for those artefacts. Two main sensor fusion approaches have been proposed: stochastic filtering, often implemented in the form of an extended Kalman filter. And the so-called “complementary filtering” approaches, which fuse multiple noisy measurements from the gyroscopes, accelerometers, and magnetometers that have complementary spectral characteristics. This chapter gives an introduction to these two approaches.