用热风速计和深度学习，实现无视觉无定位时的无人机精确定位。
Learning-based Airflow Inertial Odometry for MAVs using Thermal Anemometers in a GPS and vision denied environment
- 用GRU网络从干扰噪声中提取真实风速。
- 203秒飞行仅5.7米定位漂移，有效抑制惯性与气压计偏差。
- 适合室内或无信号环境下的微型无人机导航使用。
本文提出一种基于气流惯性的里程计系统，融合热风速计、惯性测量单元（IMU）、电调（ESC）和气压计数据。该任务极具挑战性，因低成本IMU和气压计存在显著偏置，且风速计易受螺旋桨旋转和地面效应干扰。我们采用基于门控循环单元（GRU）的深度神经网络，从噪声和扰动的风速计数据中估计相对风速，并设计带偏置模型的观测器融合多传感器数据，以估计飞行器状态。完整飞行数据（含起飞与着陆）表明，该方法可有效分离螺旋桨下洗风速与地面效应影响，在无风室内环境中准确估计飞行速度。同时，有效估计了IMU与气压计的偏置，显著降低位置积分漂移，203秒手动随机飞行仅产生5.7米误差。代码已开源：https://github.com/SyRoCo-ISIR/Flight-Speed-Estimation-Airflow。
原文摘要 · Abstract (English)
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.
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