  Quadrotors are widely used for surveillance, mapping, and deliveries. In
several scenarios the quadrotor operates in pure inertial navigation mode
resulting in a navigation solution drift. To handle such situations and bind
the navigation drift, the quadrotor dead reckoning (QDR) approach requires
flying the quadrotor in a periodic trajectory. Then, using model or learning
based approaches the quadrotor position vector can be estimated. We propose to
use multiple inertial measurement units (MIMU) to improve the positioning
accuracy of the QDR approach. Several methods to utilize MIMU data in a deep
learning framework are derived and evaluated. Field experiments were conducted
to validate the proposed approach and show its benefits.
