HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO

Understand

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.