Airflow-Inertial Odometry for Resilient State Estimation on Multirotors
This addresses the challenge of maintaining accurate pose and velocity estimates for multirotors in GPS-denied or sensor-failure scenarios, representing an incremental improvement with specific domain applications.
The paper tackles the problem of resilient state estimation on multirotors during position sensor failures by using a dead reckoning strategy with low-cost IMU and bio-inspired airflow sensors, reducing drift by up to an order of magnitude over 30 seconds compared to IMU-only methods in non-windy environments.
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.