Performance Evaluation of IMU Filtering Techniques in Yaw-Pitch-Roll Calculations
Abstract views: 23
/ 
PDF downloads: 102
Keywords:
IMU, Sensor Fusion, Madgwick Filter, Mahony Filter, Complementary Filter, Embedded SystemAbstract
This study provides a comparative analysis of the performance of three popular filtering
techniques Madgwick, Mahony, and Complementary filters used for processing data obtained from
Inertial Measurement Unit (IMU) sensors. The effectiveness of these filters in estimating Euler angles,
also known as yaw, pitch, and roll, was evaluated in terms of memory usage, processing time, and
accuracy. Separate programs were developed for each filtering technique and tested under various
scenarios. The experimental results reveal the strengths and weaknesses of each filter. It was observed
that the Madgwick filter generally provided higher accuracy but required more computational power. The
Mahony filter performed better in fast dynamic movements, while the Complementary filter was found to
be suitable for applications with lower computational requirements. This study offers guidance in
selecting the most appropriate filtering technique for IMU-based orientation estimation applications and
provides new perspectives for future research.
Downloads
References
Jouybari, A., Amiri, H., Ardalan, A., & zahraee, N. (2019). Methods comparison for attitude determination of a lightweight buoy by raw data of IMU. Measurement. https://doi.org/10.1016/J.MEASUREMENT.2018.11.061.
Song, S., Pei, Y., & Hsiao-Wecksler, E. (2022). Estimating Relative Angles Using Two Inertial Measurement Units Without Magnetometers. IEEE Sensors Journal, 22, 19688-19699. https://doi.org/10.1109/JSEN.2022.3203346.
Ludwig, S., & Burnham, K. (2018). Comparison of Euler Estimate using Extended Kalman Filter, Madgwick and Mahony on Quadcopter Flight Data. 2018 International Conference on Unmanned Aircraft Systems (ICUAS), 1236-1241. https://doi.org/10.1109/ICUAS.2018.8453465.
Madgwick, S. (2010). An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK), 25, 113-118.
Parikh, D., Vohra, S., & Kaveshgar, M. (2021). Comparison of Attitude Estimation Algorithms With IMU Under External Acceleration. 2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), 123-126. https://doi.org/10.1109/iSES52644.2021.00037.
Jouybari, A., Ardalan, A., & Rezvani, M. (2017). EXPERIMENTAL COMPARISON BETWEEN MAHONEY AND COMPLEMENTARY SENSOR FUSION ALGORITHM FOR ATTITUDE DETERMINATION BY RAW SENSOR DATA OF XSENS IMU ON BUOY. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 497-502. https://doi.org/10.5194/ISPRS-ARCHIVES-XLII-4-W4-497-2017.
Liu, M., Cai, Y., Zhang, L., & Wang, Y. (2021). Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter. Micromachines, 12. https://doi.org/10.3390/mi12111373.
Jouybari, A., Ardalan, A., & Rezvani, M. (2017). EXPERIMENTAL COMPARISON BETWEEN MAHONEY AND COMPLEMENTARY SENSOR FUSION ALGORITHM FOR ATTITUDE DETERMINATION BY RAW SENSOR DATA OF XSENS IMU ON BUOY. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 497-502. https://doi.org/10.5194/ISPRS-ARCHIVES-XLII-4-W4-497-2017.
Romadhany, F., Triwiyatno, A., & Setiyono, B. (2013). DESAIN SENSOR MARG (MAGNETIC, ANGULAR RATE, AND GRAVITY) DENGAN METODE NON-LINEAR COMPLEMENTARY FILTER SEBAGAI NAVIGASI GERAK QUADROTOR TEKNIK ELEKTRO UNIVERSITAS DIPONEGORO. , 2, 534-541. https://doi.org/10.14710/TRANSIENT.2.3.534-541.
Quoc, D., Sun, J., Le, V., & Luo, L. (2015). Complementary Filter Performance Enhancement through Filter Gain. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8, 97-110. https://doi.org/10.14257/IJSIP.2015.8.7.10.
Parikh, D., Vohra, S., & Kaveshgar, M. (2021). Comparison of Attitude Estimation Algorithms With IMU Under External Acceleration. 2021 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), 123-126. https://doi.org/10.1109/iSES52644.2021.00037.
Wen, X., Liu, C., Huang, Z., Su, S., Guo, X., Zuo, Z., & Qu, H. (2019). A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation. Sensors (Basel, Switzerland), 19. https://doi.org/10.3390/s19061340.
Madgwick, S. O., Harrison, A. J., & Vaidyanathan, R. (2011, June). Estimation of IMU and MARG orientation using a gradient descent algorithm. In 2011 IEEE international conference on rehabilitation robotics (pp. 1-7).
Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear complementary filters on the special orthogonal group. IEEE Transactions on automatic control, 53(5), 1203-1218.
Redhyka, G., Setiawan, D., & Soetraprawata, D. (2015). Embedded sensor fusion and moving-average filter for Inertial Measurement Unit (IMU) on the microcontroller-based stabilized platform. 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 72-77. https://doi.org/10.1109/ICACOMIT.2015.7440178.
