Sensor Bias Ambiguity in GNSS-IMU Pose Estimation and its Solution

doi: 10.32560/rk.2025.1.2

Abstract

This article deals with the topic of pose and sensor bias estimation based on global navigation satellite system (GNSS) and inertial measurement unit (IMU) measurements, which is a widely
discussed topic but usually by omitting the rigorous check of state observability leading to the
suspicion of invalid assumptions. The current work underlines this suspicion by pointing out through
observability calculations that to estimate position, velocity, orientation and acceleration, and
angular rate biases of a system, orientation-related measurements should be included besides
GNSS position and velocity data. Such measurement can be the magnetic vector. Theoretical results are underlined by tests considering real flight GNSS and IMU data of a DJI M600 Pro multicopter and comparing the results to the onboard DJI pose estimator.

Keywords:

estimation and filtering vehicle pose extended Kalman filter GNSS–IMU fusion autonomous systems observers for nonlinear systems sensor fusion observability issues

How to Cite

[1]
P. Bauer, “Sensor Bias Ambiguity in GNSS-IMU Pose Estimation and its Solution”, RepTudKoz, vol. 37, no. 1, pp. 17–31, Oct. 2026.

References

[1] L. A. Aguirre, L. L. Portes, C. Letellier, „Structural, Dynamical and Symbolic Observa-bility: From Dynamical Systems to Networks,” PLOS ONE, 13. évf. 10. sz. e0206180. 2018. Online: https://doi.org/10.1371/journal.pone.0206180

[2] C. V. Angelino, V. R. Baraniello, L. Cicala, „UAV Position and Attitude Estimation Using IMU, GNSS and Camera,” 2012 15th International Conference on Information Fusion, pp. 735–742. 2012.

[3] M. N. Cahyadi et al., „Analysis of GNSS/IMU Sensor Fusion at UAV Quadrotor for Navigation,” IOP Conference Series: Earth and Environmental Science, 1276(1), p. 012021. 2023. Online: https://doi.org/10.1088/1755-1315/1276/1/012021

[4] H-J. Chu et al., „GPS/MEMS INS Data Fusion and Map Matching in Urban Areas,” Sensors, 13. évf. 9. sz. pp. 11280–11288. 2013. Online: https://doi.org/10.3390/s130911280

[5] J. Chou, „Quaternion Kinematic and Dynamic Differential Equations,” IEEE Transac-tions on Robotics and Automation, 8. évf. 1. sz. pp. 53–64. 1992. Online: https://doi.org/10.1109/70.127239

[6] DJI M600 Pro Hexacopter. 2017. Online: https://www.dji.com/hu/matrice600-pro

[7] M. Farkas, Position and Attitude Estimation Ofmoving Platforms Using a Tightly Coup-led Sensor Fusion, Ph.D. Thesis. 2025. Online: https://repozitorium.omikk.bme.hu/bitstreams/f8af0efe-fb2f-45d6-9876-4dd3d084d02a/download

[8] D. Gebre-Egziabher, R. Hayward, J. Powell, „A Low-Cost GPS/Inertial Attitude He-ading Reference System (AHRS) for General Aviation Applications,” IEEE 1998 Po-sition Location and Navigation Symposium (Cat. No.98CH36153), pp. 518–525. 1998. Online: https://doi.org/10.1109/PLANS.1998.670207

[9] D. Gebre-Egziabher, Design and Performance Analysis of a Low-Cost Aided Dead Rec-koning Navigator, Ph.D. Thesis, California, Stanford University, 2002. Online: https://web.stanford.edu/group/scpnt/gpslab/pubs/theses/DemozGebreEgziahberThesis01.pdf

[10] D. Gebre-Egziabher, S. Gleason, GNSS Applications and Methods, Artech House, 2009.

[11] K. Gustavsson, UAV Pose Estimation using SensorFusion of Inertial, Sonar and Satellite Signals, Master’s Thesis, Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Techno-logy, Division of Systems and Control, 2015. Online: https://uu.diva-portal.org/smash/get/diva2:827236/FULLTEXT01.pdf

[12] C. Jekeli, Inertial Navigation Systems with Geodetic Applications, Berlin, New York, De Gruyter, 2001.

[13] A. Nemra, N. Aouf, „Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering,” IEEE Sensors Journal, 10. évf. 4. sz. pp. 789–798. 2010. Online: https://doi.org/10.1109/JSEN.2009.2034730

[14] M. L. Sollie, T. H. Bryne, T. A. Johansen, „Pose Estimation of UAVs Based on INS Aided by Two Independent Low-Cost GNSS Receivers,” 2019 International Conferen-ce on Unmanned Aircraft Systems (ICUAS), pp. 1425–1435. 2019. Online: https://doi.org/10.1109/ICUAS.2019.8797746

[15] R. Wolf et al., An Integrated Low Cost GPS/INS Attitude Determination and Position Location System, Munich, Institute of Geodesy and Navigation (IfEN), University FAF, 2016.

Downloads

Download data is not yet available.