论文标题
DIDO：深惯性四项动力学探测器
DIDO: Deep Inertial Quadrotor Dynamical Odometry
论文作者
论文摘要
在这项工作中，我们为具有深度神经网络处理的四型旋转器提出了一个仅感知性的状态估计系统，其中四个动力学被认为是对惯性运动学的感知补充。为了提高多传感器融合的精度，我们在现实世界中四型飞行数据上训练级联的网络，以学习IMU运动学特性，四型动态特征和四方面的运动状态以及其不确定性信息。这些编码的信息使我们能够解决IMU偏置稳定性，四次动力学和多传感器校准的问题。上述多源信息将其融合到两个阶段的扩展Kalman过滤器（EKF）框架中，以进行更好的估计。实验证明了我们提议的工作比几种常规和基于学习的方法的优势。
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.
