Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Attitude-Estimation-Free GNSS and IMU Integration (2304.10142v2)

Published 20 Apr 2023 in cs.RO

Abstract: A global navigation satellite system (GNSS) is a sensor that can acquire 3D position and velocity in an earth-fixed coordinate system and is widely used for outdoor position estimation of robots and vehicles. Various GNSS/inertial measurement unit (IMU) integration methods have been proposed to improve the accuracy and availability of GNSS positioning. However, all these methods require the addition of a 3D attitude to the estimated state to fuse the IMU data. In this study, we propose a new optimization-based positioning method for combining GNSS and IMU that does not require attitude estimation. The proposed method uses two types of constraints: one is a constraint between states using only the magnitude of the 3D acceleration observed by an accelerometer, and the other is a constraint on the angle between the velocity vectors using the angular change measured by a gyroscope. The evaluation results with the simulation data show that the proposed method maintains the position estimation accuracy even when the IMU mounting position error increases and improves the accuracy when the GNSS observations contain multipath errors or missing data. The proposed method could improve positioning accuracy in experiments using IMUs acquired in real environments.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. P. Misra and P. Enge, “Global Positioning System: Signals, Measurements and Performance Second Edition.”
  2. L. Serrano, D. Kim, R. B. Langley, K. Itani, and M. Ueno, “A GPS velocity sensor: How accurate can it be? - A first look,” in Proceedings of the National Technical Meeting, Institute of Navigation, vol. 2004, 2004, pp. 875–885.
  3. N. Kubo, “Advantage of velocity measurements on instantaneous RTK positioning,” GPS solutions, vol. 13, no. 4, pp. 271–280, 2009.
  4. S. Madgwick et al., “An efficient orientation filter for inertial and inertial/magnetic sensor arrays,” Report x-io and University of Bristol (UK), vol. 25, pp. 113–118, 2010.
  5. W. Xiwei, X. Bing, W. Cihang, G. Yiming, and L. Lingwei, “Factor graph based navigation and positioning for control system design: A review,” Chinese Journal of Aeronautics, vol. 35, no. 5, pp. 25–39, 2022.
  6. Z. Gong, R. Ying, F. Wen, J. Qian, and P. Liu, “Tightly Coupled Integration of GNSS and Vision SLAM Using 10-DoF Optimization on Manifold,” IEEE Sensors Journal, vol. 19, no. 24, pp. 12 105–12 117, 2019.
  7. T. Qin, S. Cao, J. Pan, and S. Shen, “A general optimization-based framework for global pose estimation with multiple sensors,” arXiv preprint arXiv:1901.03642, 2019.
  8. L. Chang, X. Niu, T. Liu, J. Tang, and C. Qian, “GNSS/INS/LiDAR-SLAM Integrated Navigation System Based on Graph Optimization,” Remote Sensing, vol. 11, no. 9, 2019.
  9. H. Chen, W. Wu, S. Zhang, C. Wu, and R. Zhong, “A GNSS/LiDAR/IMU Pose Estimation System Based on Collaborative Fusion of Factor Map and Filtering,” Remote Sensing, vol. 15, no. 3, 2023.
  10. A. Stateczny, C. Specht, M. Specht, D. Brčić, A. Jugović, S. Widźgowski, M. Wiśniewska, and O. Lewicka, “Study on the positioning accuracy of GNSS/INS systems supported by DGPS and RTK receivers for hydrographic surveys,” Energies, vol. 14, no. 21, p. 7413, 2021.
  11. H. Qi and J. B. Moore, “Direct Kalman filtering approach for GPS/INS integration,” IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 2, pp. 687–693, 2002.
  12. W. Ding, J. Wang, C. Rizos, and D. Kinlyside, “Improving adaptive Kalman estimation in GPS/INS integration,” The Journal of Navigation, vol. 60, no. 3, pp. 517–529, 2007.
  13. V. Indelman, S. Williams, M. Kaess, and F. Dellaert, “Factor graph based incremental smoothing in inertial navigation systems,” in 2012 15th International Conference on Information Fusion, 2012, pp. 2154–2161.
  14. W. Li, X. Cui, and M. Lu, “A robust graph optimization realization of tightly coupled GNSS/INS integrated navigation system for urban vehicles,” Tsinghua Science and Technology, vol. 23, no. 6, pp. 724–732, 2018.
  15. G. Wang, X. Xu, J. Wang, and Y. Zhu, “An enhanced INS/GNSS tightly coupled navigation system using time-differenced carrier phase measurement,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 5208–5218, 2019.
  16. W. Wen, X. Bai, Y. C. Kan, and L.-T. Hsu, “Tightly Coupled GNSS/INS Integration via Factor Graph and Aided by Fish-Eye Camera,” IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 10 651–10 662, 2019.
  17. P. Lyu, S. Bai, J. Lai, B. Wang, X. Sun, and K. Huang, “Optimal Time Difference-Based TDCP-GPS/IMU Navigation Using Graph Optimization,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–10, 2021.
  18. W. Wen, T. Pfeifer, X. Bai, and L.-T. Hsu, “Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter,” NAVIGATION, vol. 68, no. 2, pp. 315–331, 2021.
  19. C. Kilic, S. Das, E. Gutierrez, R. Watson, and J. Gross, “ZUPT Aided GNSS Factor Graph with Inertial Navigation Integration for Wheeled Robots,” in Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), 2021, pp. 3285–3293.
  20. Z. F. Syed, P. Aggarwal, X. Niu, and N. El-Sheimy, “Civilian Vehicle Navigation: Required Alignment of the Inertial Sensors for Acceptable Navigation Accuracies,” IEEE Transactions on Vehicular Technology, vol. 57, no. 6, pp. 3402–3412, 2008.
  21. Z. Bao, G. Lu, Y. Wang, and D. Tian, “A Calibration Method for Misalignment Angle of Vehicle-mounted IMU,” Procedia - Social and Behavioral Sciences, vol. 96, pp. 1853–1860, 2013.
  22. F. Dellaert, “Factor Graphs and GTSAM: A Hands-on Introduction,” Tech. Rep., 2012.
  23. X. Bai, W. Wen, and L.-T. Hsu, “Time-Correlated Window-Carrier-Phase-Aided GNSS Positioning Using Factor Graph Optimization for Urban Positioning,” IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 4, pp. 3370–3384, 2022.
  24. F. Dellaert and G. Contributors, “borglab/gtsam,” May 2022. [Online]. Available: https://github.com/borglab/gtsam)
  25. T. Lupton and S. Sukkarieh, “Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions,” IEEE Transactions on Robotics, vol. 28, no. 1, pp. 61–76, 2012.
  26. C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, “IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation.”   Georgia Institute of Technology, 2015.
  27. G. M. Fu, M. Khider, and F. van Diggelen, “Android Raw GNSS Measurement Datasets for Precise Positioning,” in Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), 2020, pp. 1925–1937.
  28. J. Rehder, J. Nikolic, T. Schneider, T. Hinzmann, and R. Siegwart, “Extending kalibr: Calibrating the extrinsics of multiple IMUs and of individual axes,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 4304–4311.
  29. H. Sharma, M. Bochkati, and T. Pany, “Time-Synchronized GNSS/IMU Data Logging from Android Smartphone and its Influence on the Positioning Accuracy,” in Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), 09 2021.
Citations (4)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now