Abstract

To determine the position and navigation of an unknown environment, UAVs rely on sensors that provide information regarding position, speed, and orientation. There are sensors to provide direct navigation information such as the Global Navigation Satellite System (GNSS) by providing position data, or indirect sensors such as inertial sensors which provide speed and orientation data. An inertial sensor or commonly known as an Inertial Measurement Unit (IMU) is a combination of data acceleration (accelerometer) and angular velocity (gyroscope). By performing GNSS/IMU sensor fusion at UAV Quadrotor will increase the accuracy of aircraft localization based on its mathematical model involving the Kalman Filter approach. The main goal is to improve the coordinates obtained from Quadrotor UAV measurements, so that position of UAV Quadrotor aircraft is more accurate. Raw data of sensors GNSS/IMU is obtained during the flight of the aircraft. Visual comparison is used to determine whether the coordinate of the processed data has better accuracy than the raw data. The results showed that the Unscented Kalman Filter (UKF) simulation gave 3D position accuracy of 0.403 m to the measurement data. It can improve 23,47% fprm EKF Estimation which give 3D position accuracy of 16.598 m.

Original languageEnglish
Article number012021
JournalIOP Conference Series: Earth and Environmental Science
Volume1276
Issue number1
DOIs
Publication statusPublished - 2023
Event8th Geomatics International Conference, GeoICON 2023 - Surabaya, Indonesia
Duration: 27 Jul 2023 → …

Keywords

  • Extended Kalman Filter
  • GNSS
  • IMU
  • UAV Quadrotor
  • Unscented Kalman Filter

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