  We present a dead reckoning strategy for increased resilience to position
estimation failures on multirotors, using only data from a low-cost IMU and
novel, bio-inspired airflow sensors. The goal is challenging, since low-cost
IMUs are subject to large noise and drift, while 3D airflow sensing is made
difficult by the interference caused by the propellers and by the wind. Our
approach relies on a deep-learning strategy to interpret the measurements of
the bio-inspired sensors, a map of the wind speed to compensate for
position-dependent wind, and a filter to fuse the information and generate a
pose and velocity estimate. Our results show that the approach reduces the
drift with respect to IMU-only dead reckoning by up to an order of magnitude
over 30 seconds after a position sensor failure in non-windy environments, and
it can compensate for the challenging effects of turbulent, and spatially
varying wind.
