AirIO: IMU特徴の可観測性を高めた慣性オドメトリ学習
AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability
生のIMUデータを機体座標系のまま扱い姿勢情報も明示的に入力することで、UAVの動的な飛行でも高精度に自己位置推定できる学習型慣性オドメトリを提案した。
著者: Yuheng Qiu, Can Xu, Yutian Chen, Shibo Zhao, Junyi Geng, Sebastian Scherer
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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.
