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）是利用仅有机载传感器估计自主微型飞行器状态的最常用方法。现有方法通过将动力学模型纳入估计流程来提升VIO性能，然而此类方法在低保真度飞行器模型及持续外部扰动（如风力）条件下性能会退化。我们提出的HDVIO方法通过采用混合动力学模型克服了这些限制，该模型将质点飞行器模型与捕获复杂气动效应的学习成分相结合。HDVIO利用实际运动与混合动力学模型预测运动之间的偏差，对外部力和完整机器人状态进行估计。其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。为展示方法性能，我们在公开及新型无人机动力学数据集上给出结果，并展示了四旋翼飞行器在高达25 km/h强风环境中的真实实验。结果表明，与现有最优方法相比，我们的方法将运动估计与外部力估计的性能分别提升最高达33%与40%。此外，与现有方法不同，我们证明了在无需明确已知飞行器完整状态的情况下，仍能准确预测其动力学行为。