🤖 AI Summary
In GPS- and vision-denied indoor environments, micro-air vehicles (MAVs) lack reliable external references for state estimation.
Method: This paper proposes an airflow-based inertial odometry system using hot-wire anemometers. It first decouples rotor downwash and ground-effect interference; designs a gated recurrent unit (GRU) network to robustly estimate relative airspeed from highly perturbed anemometer signals; and constructs a nonlinear observer jointly modeling sensor biases, enabling tightly coupled fusion of hot-wire anemometer, IMU, electronic speed controller (ESC), and barometer measurements.
Results: In a 203-second manually piloted randomized flight, position integration drift is only 5.7 m; takeoff and landing velocities are accurately estimated throughout; IMU and barometer biases are calibrated in real time; and airspeed estimation accuracy improves significantly under calm-air conditions.
📝 Abstract
This work demonstrates an airflow inertial based odometry system with multi-sensor data fusion, including thermal anemometer, IMU, ESC, and barometer. This goal is challenging because low-cost IMUs and barometers have significant bias, and anemometer measurements are very susceptible to interference from spinning propellers and ground effects. We employ a GRU-based deep neural network to estimate relative air speed from noisy and disturbed anemometer measurements, and an observer with bias model to fuse the sensor data and thus estimate the state of aerial vehicle. A complete flight data, including takeoff and landing on the ground, shows that the approach is able to decouple the downwash induced wind speed caused by propellers and the ground effect, and accurately estimate the flight speed in a wind-free indoor environment. IMU, and barometer bias are effectively estimated, which significantly reduces the position integration drift, which is only 5.7m for 203s manual random flight. The open source is available on https://github.com/SyRoCo-ISIR/Flight-Speed-Estimation-Airflow.