Pose Estimation of a Drone Using Dynamic Extended Kalman Filter Based on a Fuzzy System

被引：1
作者：
Lim, Eunsoo [1 ,2 ]
机构：
[1] Ajou Univ, IT Convergence Gradutate Sch, Elect & Informat, Seoul, South Korea
[2] ViewMagine Co, Seoul, South Korea
来源：
2021 THE 9TH INTERNATIONAL CONFERENCE ON CONTROL, MECHATRONICS AND AUTOMATION (ICCMA 2021) | 2021年
关键词：
Extended Kalman Filter; Pose Estimation; Drone; Fuzzy System; ROS; Gazebo; Pixhawk; EKF; LOCALIZATION;
D O I：
10.1109/ICCMA54375.2021.9646187
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
When conducting pose estimation of a drone in an external environment, inertial navigation systems (INSs) and global navigation satellite systems (GNSSs) are often used. Filter-based extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF) based on sampling method are used for estimating pose more accuracy. This study attempted to improve the accuracy of EKF using a fuzzy system, which controls covariance according to environment. In order to test the algorithm, ROS, Gazebo, and Pixhawk offboard firmware were used.
引用
收藏
页码：141 / 145
页数：5
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