Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter

  • Mochamad Mobed Bachtiar Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
  • Iwan Kurnianto Wibowo Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
  • Yusuf Rifa’I Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
  • Daniswara Prasetya Subagja Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
  • Nanda Alfi Syahriyah Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
Abstract views: 97 , PDF downloads: 52
Keywords: Sensor, IMU, Kalman Filter, Estimation, Prediction, ARM

Abstract

The Inertial Measurement Unit (IMU) sensor is a tool used to measure the speed and acceleration of an object in 3 dimensions (x, y, z). IMU sensors are often used in robotics, drone control, autonomous vehicles, and augmented reality applications. Usually, the data obtained from the IMU sensor is contaminated by interference and noise, which can reduce measurement accuracy. Kalman Filter is a statistical method used to combine measurement data with a mathematical system model to produce better estimates. In the IMU context, the Kalman Filter removed interference and noise affecting acceleration and speed data so that IMU sensor data could be estimated more accurately. This algorithm predicts the next data state based on previous data and updates the prediction with new measurement data. The measurement implementation in this research is the IMU sensor on the GY-91 module to determine the object's tilt on the pitch, roll, and yaw axes during flight. The ARM STM32F407VGT6 microcontroller pin reads the sensor, and then the estimation and prediction process is carried out using the Kalman filter algorithm. With the parameters Kalman Measurement Error = 1, Estimation Error = 0.12, and Covariance Process = 0.4, it can predict the reading results from the IMU sensor well.

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Published
2023-11-30
How to Cite
Mobed Bachtiar, M., Wibowo, I. K., Rifa’I, Y., Prasetya Subagja, D., & Syahriyah, N. A. (2023). Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter. International Journal of Artificial Intelligence & Robotics (IJAIR), 5(2), 63-70. https://doi.org/10.25139/ijair.v5i2.7179