DIDO: Deep Inertial Quadrotor Dynamical Odometry

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...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2022-10, Vol.7 (4), p.9083-9090
Main Authors: Zhang, Kunyi, Jiang, Chenxing, Li, Jinghang, Yang, Sheng, Ma, Teng, Xu, Chao, Gao, Fei
Format: Article
Language:English
Subjects:
Accelerometers
Aerodynamics
Artificial neural networks
Dynamic characteristics
Dynamic stability
Extended Kalman filter
Gyroscopes
Kinematics
Localization
Machine learning
Multisensor fusion
Neural networks
Rotors
sensorimotor learning
State estimation
Vehicle dynamics
visual-iner- tial SLAM
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3189168