AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability

TL;DR

This paper introduces AirIO, a learning-based inertial odometry method for UAVs that maintains IMU data in the body frame, significantly improving accuracy and robustness without external sensors.

Contribution

It proposes a novel approach that preserves IMU data in the body frame and encodes attitude information, enhancing generalization and accuracy in UAV inertial odometry.

Findings

66.7% average accuracy improvement across datasets

23.8% additional improvement with attitude encoding

Robust state estimation during aggressive UAV maneuvers

Abstract

Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Unmanned Aerial Vehicle (UAV) applications, yet existing learning-based IO models often fail to generalize to UAVs due to the highly dynamic and non-linear-flight patterns that differ from pedestrian motion. In this work, we identify that the conventional practice of transforming raw IMU data to global coordinates undermines the observability of critical kinematic information in UAVs. By preserving the body-frame representation, our method achieves substantial performance improvements, with a 66.7% average increase in accuracy across three datasets. Furthermore, explicitly encoding attitude information into the motion network results in an additional 23.8% improvement over prior results. Combined with a data-driven IMU correction model (AirIMU) and an uncertainty-aware Extended Kalman Filter (EKF), our approach ensures robust state estimation under aggressive UAV maneuvers without relying on external sensors or control inputs. Notably, our method also demonstrates strong generalizability to unseen data not included in the training set, underscoring its potential for real-world UAV applications.