  In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes)
have become widely available due to their small size and low cost. Inertial
sensor measurements are obtained at high sampling rates and can be integrated
to obtain position and orientation information. These estimates are accurate on
a short time scale, but suffer from integration drift over longer time scales.
To overcome this issue, inertial sensors are typically combined with additional
sensors and models. In this tutorial we focus on the signal processing aspects
of position and orientation estimation using inertial sensors. We discuss
different modeling choices and a selected number of important algorithms. The
algorithms include optimization-based smoothing and filtering as well as
computationally cheaper extended Kalman filter and complementary filter
implementations. The quality of their estimates is illustrated using both
experimental and simulated data.
