Visual-inertial odometry (VIO) is the most common approach for estimating the state of autonomous micro aerial vehicles using only onboard sensors. Existing methods improve VIO performance by including a dynamics model in the estimation pipeline. However, such methods degrade in the presence of low-fidelity vehicle models and continuous external disturbances, such as wind. Our proposed method, HDVIO, overcomes these limitations by using a hybrid dynamics model that combines a point-mass vehicle model with a learning-based component that captures complex aerodynamic effects. HDVIO estimates the external force and the full robot state by leveraging the discrepancy between the actual motion and the predicted motion of the hybrid dynamics model. Our hybrid dynamics model uses a history of thrust and IMU measurements to predict the vehicle dynamics. To demonstrate the performance of our method, we present results on both public and novel drone dynamics datasets and show real-world experiments of a quadrotor flying in strong winds up to 25 km/h. The results show that our approach improves the motion and external force estimation compared to the state-of-the-art by up to 33% and 40%, respectively. Furthermore, differently from existing methods, we show that it is possible to predict the vehicle dynamics accurately while having no explicit knowledge of its full state.

翻译：视觉-惯性里程计（VIO）是利用仅搭载机载传感器估计自主微型飞行器状态的最常用方法。现有方法通过在估计流程中引入动力学模型来提升VIO性能。然而，当存在低保真度飞行器模型以及持续外部扰动（如风力）时，此类方法性能会显著下降。我们提出的HDVIO方法通过采用混合动力学模型克服了这些局限，该模型将质点飞行器模型与捕捉复杂气动效应的学习组件相结合。HDVIO利用实际运动与混合动力学模型预测运动之间的差异，同时估计外部作用力与机器人完整状态。我们的混合动力学模型通过历史推力与IMU测量值预测飞行器动力学特性。为验证方法性能，我们在公开及新构建的无人机动力学数据集上呈现结果，并展示了四旋翼飞行器在高达25 km/h强风环境下的真实世界实验。结果表明，与现有最优方法相比，我们的方法在运动估计与外部作用力估计上分别实现了最高33%和40%的改进。此外，与现有方法不同，我们证明可在缺乏完整状态显式知识的情况下精准预测飞行器动力学特性。