Traditional IMU based PDR systems suffer from
rapidly growing drift effects due to the inherent bias of the
inertial sensor. Many existing solutions to mitigate this problem
use aiding sensors or information as heuristics or map data. We
propose a new optimization framework to solve the PDR estimation
problem where the sensors biases are explicitly included as
state variables and therefore be used to correct for bias effects
in the PDR. By using a smoothing approach and exploiting the
rigid structure of a MIMU array one can solve for the slowly
varying sensor biases. This paper presents the method and gives
an exemplary result of a walking trial. Good agreements in the
position and orientation with an optical reference system were
found. Moreover, accelerometer and gyroscope biases could be
estimated accordingly. Further research includes the performance
of more experiments under various conditions such that a
more quantitative evaluation can be obtained. In addition, an
exploration of a (pseudo) realtime filter version would be valuable
such that the system can be applied online.