在无GPS和视觉环境下基于学习的、使用热风速计的微型飞行器（MAV）气流惯性里程计
Learning-based Airflow Inertial Odometry for MAVs using Thermal Anemometers in a GPS and vision denied environment
AI总结：
针对无GPS和视觉环境下MAV测速难题，提出融合热风速计等多传感器的气流惯性里程计方案，用GRU网络估计空速、带偏差模型的观测器融合数据，有效降低位置漂移，且代码开源。
摘要：
本研究展示了一套融合多传感器数据的气流惯性里程计系统，涉及的传感器包括热风速计、IMU（惯性测量单元）、ESC（电子调速器）和气压计。该研究目标颇具挑战性，因为低成本IMU和气压计存在显著偏差，且风速计测量值极易受旋转螺旋桨的干扰及地面效应影响。我们采用基于GRU（门控循环单元）的深度神经网络，从含噪声且受干扰的风速计测量值中估计相对空速，并搭配带偏差模型的观测器融合传感器数据，进而估计飞行器状态。包含地面起飞与降落的完整飞行数据表明，该方法能够分离螺旋桨下洗气流引发的风速与地面效应，在无风室内环境中准确估计飞行速度。IMU和气压计的偏差得到有效估计，大幅降低了位置积分漂移——在203秒的手动随机飞行中，位置漂移仅为5.7米。该项目开源代码可在https://github.com/SyRoCo-ISIR/Flight-Speed-Estimation-Airflow 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
