Airflow-Inertial Odometry for Resilient State Estimation on Multirotors

TL;DR

This paper introduces a resilient dead reckoning method for multirotors that combines bio-inspired airflow sensors, deep learning, and sensor fusion to improve position estimation accuracy in challenging wind conditions.

Contribution

It presents a novel airflow-inertial odometry approach using bio-inspired sensors and deep learning to enhance multirotor resilience against position estimation failures.

Findings

Reduces drift by up to ten times compared to IMU-only methods.

Effective in turbulent and spatially varying wind conditions.

Maintains accurate pose estimates after sensor failure for at least 30 seconds.

Abstract

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.