# HDVIO2.0: Wind and Disturbance Estimation with Hybrid Dynamics VIO

arXiv: https://arxiv.org/abs/2504.00969 · HTML: https://mlsanity.com/paper/2504.00969/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [TossingBot: Learning to Throw Arbitrary Objects with Residual Physics](https://mlsanity.com/paper/1903.11239/) (2019)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [NeuroBEM: Hybrid Aerodynamic Quadrotor Model](https://mlsanity.com/paper/2106.08015/) (2021)
- [Quaternion kinematics for the error-state Kalman filter](https://mlsanity.com/paper/1711.02508/) (2017)
- [Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight](https://mlsanity.com/paper/2307.06100/) (2023)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight](https://mlsanity.com/paper/2309.09865/) (2023)
- [Sim-to-Real of Soft Robots with Learned Residual Physics](https://mlsanity.com/paper/2402.01086/) (2024)
- [Learned Inertial Odometry for Autonomous Drone Racing](https://mlsanity.com/paper/2210.15287/) (2022)
- [Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study](https://mlsanity.com/paper/2202.08894/) (2022)
- [Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents](https://mlsanity.com/paper/2201.02569/) (2022)
- [Range, Endurance, and Optimal Speed Estimates for Multicopters](https://mlsanity.com/paper/2109.04741/) (2021)
- [Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor](https://mlsanity.com/paper/2003.02305/) (2020)
- [VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation](https://mlsanity.com/paper/2011.03993/) (2020)
- [HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO](https://mlsanity.com/paper/2306.11429/) (2023)
- [The Visual-Inertial-Dynamical Multirotor Dataset](https://mlsanity.com/paper/2103.11152/) (2021)
- [Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework](https://mlsanity.com/paper/2412.03136/) (2024)

## Abstract (arXiv)

Visual-inertial odometry (VIO) is widely used for state estimation in autonomous micro aerial vehicles using onboard sensors. Current methods improve VIO by incorporating a model of the translational vehicle dynamics, yet their performance degrades when faced with low-accuracy vehicle models or continuous external disturbances, like wind. Additionally, incorporating rotational dynamics in these models is computationally intractable when they are deployed in online applications, e.g., in a closed-loop control system. We present HDVIO2.0, which models full 6-DoF, translational and rotational, vehicle dynamics and tightly incorporates them into a VIO with minimal impact on the runtime. HDVIO2.0 builds upon the previous work, HDVIO, and addresses these challenges through a hybrid dynamics model combining a point-mass vehicle model with a learning-based component, with access to control commands and IMU history, to capture complex aerodynamic effects. The key idea behind modeling the rotational dynamics is to represent them with continuous-time functions. HDVIO2.0 leverages the divergence between the actual motion and the predicted motion from the hybrid dynamics model to estimate external forces as well as the robot state. Our system surpasses the performance of state-of-the-art methods in experiments using public and new drone dynamics datasets, as well as real-world flights in winds up to 25 km/h. Unlike existing approaches, we also show that accurate vehicle dynamics predictions are achievable without precise knowledge of the full vehicle state.