  This paper describes a calibration algorithm to simultaneously calibrate a
magnetometer and an accelerometer without any information besides the sensors
readings. Using a linear sensor model and maximum likelihood cost, the
algorithm is able to estimate both sensors' biases, gains, and covariances,
besides sensor orientations and Earth's fields. Results show errors of less
than 0.1 standard deviations in simulation, and high-quality estimates with
real sensors even when the algorithm's assumptions are violated.
