HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO (RSS 2023)

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 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 vehicle dynamics accurately while having no explicit knowledge of its full state.
Reference:
Giovanni Cioffi*, Leonard Bauersfeld*, Davide Scaramuzza
HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO
Robotics: Science and Systems (RSS), 2023, Daegu, South Korea
PDF: rpg.ifi.uzh.ch...
Our research page on visual-inertial odometry: rpg.ifi.uzh.ch...
Our research page on agile drone flight: rpg.ifi.uzh.ch...
Our research page on vision-based drone navigation: rpg.ifi.uzh.ch...
Affiliation:
Giovanni Cioffi, Leonard Bauersfeld, and D. Scaramuzza are with the Robotics and Perception Group, Dept. of Informatics, University of Zurich, and Dept. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland rpg.ifi.uzh.ch/

Thank you for sharing.

[@snakehaihai]

good job !