Abstract
Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aggarwal P, Gu D, Nassar S, Syed Z, El-Sheimy N (2007) Extended particle filter (EPF) for INS/GPS land vehicle navigation applications. In: Proceedings of the 20th international technical meeting of the satellite division of the institute of navigation (ION GNSS 2007), Fort Worth, Texas, USA, September 2007, pp 2619–2626
Aggarwal P, Syed Z, El-Sheimy N (2008) Hybrid extended particle filter (HEPF) for integrated civilian navigation system. In: Proceedings of IEEE/ION PLANS 2008, Monterey, California, USA, May 2008, pp 984–992
Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2)
CarChip (2007) OBDII-based vehicle data logger and software: Davis instruments
Carlson R, Gerdes J, Powell J (2004) Error sources when land vehicle dead reckoning with differential wheelspeeds. Navigation 51(1):13–27
Caron F, Davy M, Duflos E, Vanheeghe P (2007) Particle filtering for multisensor data fusion with switching observation models: application to land vehicle positioning. IEEE Trans Signal Process 55(6):2703–2719
Dellaert F, Fox D, Burgard W, Thrun S (1999) Monte carlo localization for mobile robots. In: Proceedings of the IEEE international conference on robotics and automation (ICRA-99), Detroit, MI, USA
Doucet A, Godsill SJ, Andrieu C (2000) On sequential monte carlo sampling methods for Bayesian filtering. Stat Comput 10:197–208
Dyckman HL, Sloat S, Pettus B (2004) Particle filtering to improve GPS/INS integration. In: Proceedings of the ION GNSS 2004, Long Beach, CA, USA, September 2004, pp 1619–1626
Farrell JA (2008) Aided navigation: GPS with high rate sensors. McGraw Hill, NY
Giremus A, Doucet A, Calmettes V, Tourneret J-Y (2004) A Rao-Blackwellized particle filter for INS/GPS integration. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, vol 3, pp iii 964–967
Giremus A, Tourneret J-Y, Djuric PM (2005) An improved regularized particle filter for GPS/INS integration. In: The sixth IEEE international workshop on signal processing advances in wireless communications (SPAWC 2005), New York, NY, USA, June 2005, pp 1013–1017
Godha S, Cannon E (2007) GPS/MEMS INS integrated system for navigation in urban areas. GPS Solut 11(3):193–203
Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE proceedings-F (Radar and Signal Processing), vol 140, pp 107–113
Grewal MS, Weill LR, Andrews AP (2007) Global positioning systems, inertial navigation, and integration. Wiley, New York
Groves PD (2008) Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech House
HG1700 (2006) Inertial measurement unit: Honeywell
IMU300—6DOF inertial measurement unit: Crossbow Technology Inc (2006)
Jensfelt P (2001) Approaches to mobile robot localization in indoor environments. Ph.D. thesis, Department of Sensors, signals and systems, Royal Institute of Technology, Sweden
Jensfelt P, Wijk O, Austin D, Andersson M (2000) Experiments on augmenting condensation for mobile robot localization. In: Proceedings of the IEEE international conference on robotics and automation (ICRA-2000), vol 3, San Francisco, CA, USA, pp 2518–2524
Lassen SQ GPS Receiver—system designer reference manual: Trimble Navigation Limited (2002)
Noureldin A, Irvine-Halliday D, Mintchev MP (2002) Accuracy limitations of FOG-based continuous measurement-while-drilling surveying instruments for horizontal wells. IEEE Trans Instrum Meas 51(6):1177–1191
Noureldin A, Irvine-Halliday D, Mintchev MP (2004) Measurement-while- drilling surveying of highly-inclined and horizontal well sections utilizing single-axis gyro sensing system. Meas Sci Technol IoP 15(12):2426–2434
Noureldin A, Karamat TB, Eberts MD, El-Shafie A (2009) Performance enhancement of MEMS based INS/GPS integration for low cost navigation applications. IEEE Trans Veh Technol 58(3):1077–1096
Quddus M, Ochieng W, Noland R (2006) Integrity of map-matching algorithms. Transp Res C Emerg Technol 14(4):283–302
Skog I, Handel P (2009) In-car positioning and navigation—a survey. IEEE Trans Intell Transp Syst 10(1):4–21
SPAN Technology System User Manual OM-20000062: NovAtel Inc (2005)
Thrun S, Fox D, Burgard W, Dellaert F (2001) Robust monte carlo localization for mobile robots. Artif Intell 128:99–141
Wilson J (2007) Low-cost PND dead reckoning using automotive diagnostic links. In: Proceedings of the 20th international technical meeting of the satellite division of the Institute of Navigation, (ION GNSS 2007), Fort Worth, Texas, USA, September 2007, pp 2066–2074
Yi Y, Grejner-Brzezinska DA (2006) Tightly-coupled GPS/INS integration using unscented Kalman filter and particle filter. In: Proceedings of the Institute of Navigation—19th international technical meeting of the satellite division (ION GNSS 2006), vol 4, Fort Worth, TX, USA, September 2006, pp 2182–2191
Acknowledgments
This research was supported in part by research grants from Natural Sciences and Engineering Research Council (NSERC), Geomatics for Informed Decision (GEOIDE) Network Centers of Excellence, and Defense Research and Development Canada (DRDC) Ottawa. The equipment was acquired by research funds from Canada Foundation for Innovation, Ontario Innovation Trust and the Royal Military College of Canada.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Georgy, J., Karamat, T., Iqbal, U. et al. Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter. GPS Solut 15, 239–252 (2011). https://doi.org/10.1007/s10291-010-0186-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10291-010-0186-4