DIDO: 深層学習を用いた慣性情報のみによるクアッドロータの動力学オドメトリ
DIDO: Deep Inertial Quadrotor Dynamical Odometry
クアッドロータの慣性計測のみから状態推定を行うため、実飛行データでIMU特性・機体動特性・運動状態と不確かさを学習し、2段階EKFで融合する手法を提案した。
著者: Kunyi Zhang, Chenxing Jiang, Jinghang Li, Sheng Yang, Teng Ma, Chao Xu, Fei Gao
分類: cs.RO
原文アブストラクト
In this work, we propose an interoceptive-only state estimation system for a quadrotor with deep neural network processing, where the quadrotor dynamics is considered as a perceptive supplement of the inertial kinematics. To improve the precision of multi-sensor fusion, we train cascaded networks on real-world quadrotor flight data to learn IMU kinematic properties, quadrotor dynamic characteristics, and motion states of the quadrotor along with their uncertainty information, respectively. This encoded information empowers us to address the issues of IMU bias stability, quadrotor dynamics, and multi-sensor calibration during sensor fusion. The above multi-source information is fused into a two-stage Extended Kalman Filter (EKF) framework for better estimation. Experiments have demonstrated the advantages of our proposed work over several conventional and learning-based methods.
