AI-IO: An Aerodynamics-Inspired Real-Time Inertial Odometry for Quadrotors

TL;DR

This paper introduces AI-IO, a physics-inspired transformer-based inertial odometry system for quadrotors that leverages rotor speed data to significantly improve velocity prediction accuracy and robustness in real-time applications.

Contribution

The paper presents a novel aerodynamics-inspired transformer model incorporating rotor speed measurements for improved inertial odometry accuracy and generalization in quadrotor flight.

Findings

Velocity prediction improved by 36.9%

Additional 22.4% accuracy gain over previous methods

Validated on real-world datasets and real-time systems

Abstract

Inertial Odometry (IO) has gained attention in quadrotor applications due to its sole reliance on inertial measurement units (IMUs), attributed to its lightweight design, low cost, and robust performance across diverse environments. However, most existing learning-based inertial odometry systems for quadrotors either use only IMU data or include additional dynamics-related inputs such as thrust, but still lack a principled formulation of the underlying physical model to be learned. This lack of interpretability hampers the model's ability to generalize and often limits its accuracy. In this work, we approach the inertial odometry learning problem from a different perspective. Inspired by the aerodynamics model and IMU measurement model, we identify the key physical quantity--rotor speed measurements required for inertial odometry and design a transformer-based inertial odometry. By incorporating rotor speed measurements, the proposed model improves velocity prediction accuracy by 36.9%. Furthermore, the transformer architecture more effectively exploits temporal dependencies for denoising and aerodynamics modeling, yielding an additional 22.4% accuracy gain over previous results. To support evaluation, we also provide a real-world quadrotor flight dataset capturing IMU measurements and rotor speed for high-speed motion. Finally, combined with an uncertainty-aware extended Kalman filter (EKF), our framework is validated across multiple datasets and real-time systems, demonstrating superior accuracy, generalization, and real-time performance. We share the code and data to promote further research (https://github.com/SJTU-ViSYS-team/AI-IO).