Hostname: page-component-74656f9884-8r8nv Total loading time: 0 Render date: 2026-11-10T05:50:15.360Z Has data issue: false hasContentIssue false
  • English
  • Français

Precise outdoor localization with a GPS–INS integration system

Published online by Cambridge University Press:  26 July 2012

Wonkyo Seo ,
Seoyoung Hwang ,
Jaehyun Park  and
Jang-Myung Lee
Wonkyo Seo
Affiliation:
School of Electrical Engineering, Pusan National University, Busan, South Korea
Seoyoung Hwang
Affiliation:
School of Electrical Engineering, Pusan National University, Busan, South Korea
Jaehyun Park
Affiliation:
School of Electrical Engineering, Pusan National University, Busan, South Korea
Jang-Myung Lee*
Affiliation:
School of Electrical Engineering, Pusan National University, Busan, South Korea
*
*E-mail: jmlee@pusan.ac.kr
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported.

Keywords

Robot localizationMobile robotsControl of robotic systemsMotion planningNavigation

Information

Type
Articles
Information
Robotica , Volume 31 , Issue 3 , May 2013 , pp. 371 - 379
Copyright
Copyright © Cambridge University Press 2012

References

1.Soloviev, A. and van Graas, F., “Enhancement of integrated GPS/INS performance utilizing frequency domain implementation of INS calibration,” J. Inst. Navig. 54 (2) (2007, Feb.).CrossRefGoogle Scholar
2.Liu, H., Nassar, S. and Sheimy, N. E., “Two-filter smoothing for accurate INS/GPS land-vehicle navigation in urban centers,” IEEE Trans. Veh. Technol. 59 (9), 4256–4267 (2010).CrossRefGoogle Scholar
3.Han, S. and Wang, J., “Land vehicle navigation with the integration of GPS and reduced INS: Performance improvement with velocity aiding,” Robotica 63 (1), 153–166 (2010).Google Scholar
4.Kim, S., Roh, C., Kang, S. and Park, M., “Outdoor Navigation of a Mobile Robot Using Differential GPS and Curb Detection,” In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Roma, Italy (Apr. 10–14, 2007) pp. 3414–3419.CrossRefGoogle Scholar
5.Schmidt, G. T., INS/GPS Technology Trends (NATO Research and Technology Organization, France, May 2009).Google Scholar
6.Lecce, V. Di., Amato, A. and Calabrese, M., “GPS-Aided Lightweight Architecture to Support Multi-Sensor Data Synchronization,” In: Proceedings of 2008 IEEE Instrumentation and Measurement Technology Conference (IMTC 2008), Canada (2008) pp. 149–154.CrossRefGoogle Scholar
7.Groves, P. D., Handley, R. J. and Runnalls, A. R., “Optimising the integration of terrain referenced navigation with INS and GPS,” Robotica 59 (1), 71–89 (2006).Google Scholar
8.Gang, L., Jie, Y., Lixin, W., Jiang, G. and Xiaomei, W., “Detecting and Extracting Vibration Disturb in IMU Testing in Field”, In: Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET), Chengdu, China (2010) pp. V2516–2518.Google Scholar
9.Dong, H. H., Lin, H. X., Ming, L. M. and Yue, S. Z., “Federated unscented particle filtering algorithm for SINS/CNS/GPS system,” J. Central South Univ. Technol. 17 (4), 778–785 (2010).Google Scholar
10.Yi, J., Wang, H., Zhang, J., Song, D., Jayasuriya, S. and Liu, J., “Kinematic modeling and analysis of skid-steered mobile robots with applications to low-cost inertial-measurement-unit-based motion estimation,” IEEE Trans. Robot. on 25 (5), 1087–1097 (2009).Google Scholar
11.Baselga, S., Asenjo, L. G., Garrigues, P. and Lerma, J. L., “Inertial navigation system data filtering prior to GPS/INS integration,” Robotica 62 (4), 711–720 (2009).Google Scholar
12.Zhou, J., Knedlik, S. and Loffeld, O., “INS/GPS tightly coupled integration using adaptive unscented particle filter,” Robotica 63 (3), 491–511 (2010).Google Scholar
13.Lee, J. K. and Jekeli, C., “Rao-blackwellized unscented particle filter for a handheld unexploded ordnance geolocation system using IMU/GPS,” Robotica 64 (2), 327–340 (2011).Google Scholar
14.Barralon, P., Vuillerme, N. and Noury, N., “Walk Detection With a Kinematic Sensor: Frequency and Wavelet Comparison,” In: Proceedings of the IEEE 2006 International Conference of the Engineering in Medicine and Biology Society (EMBS '06), New York, USA (2006) pp. 1711–1714.CrossRefGoogle Scholar
15.Young, R. K., Wavelet Theory and Its Applications (Kluwer, the Netherlands, 1993).CrossRefGoogle Scholar
16.Chan, A. K. and Peng, C., Wavelets for Sensing Technologies (Artech, London, 2003).Google Scholar
17.Rock, S. M., Powell, J. D. and Enge, P., Design and Performance of a Robust GPS/INS Attitude System for Automobile Application (Ph.D. Dissertation (Stanford University, Stanford, CA, 2004).Google Scholar
18.Ristic, B., Arulampalam, S. and Gordon, N., Beyond the Kalman Filter (Artech, London, 2004).Google Scholar
19.Lee, J. K., Cacamise, D. J. II, Jekeli, C., “The Precise INS/GPS Geolocation of a Vehicle-Towed UXO Detection System,” In: Proceedings of the Position Location and Navigation Symposium (PLANS, 2010) IEEE/ION, South Carolina (2010) pp. 1189–1195.CrossRefGoogle Scholar
20.Tian, L., Rong, J., Zhong, X. and Li, T., “UPF Algorithm and its Application in the GPS/INS Integrated Navigation,” In: Proceedings of International Conference on Wireless Communications & Signal Processing, 2009 (WCSP 2009), Nanjing, China (2009) pp. 1–4.Google Scholar
21.Yang, S., Ma, Q. and Huang, W., “Particle Swarm Optimized Unscented Particle Filter for Target Tracking,” In: Proceedings of the 2nd International Congress on Image and Signal Processing, 2009 (CISP '09), Tianjin, China (2009) pp. 1–5.Google Scholar
22.Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T., “A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking,” IEEE Trans. Signal Process. 50 (2), 174–188 (2002).CrossRefGoogle Scholar
23.Haykin, S., Kalman Filtering and Neural Networks (Wiley, Colorado, 2001) pp. 228–229.CrossRefGoogle Scholar
24.Chen, Z., “Bayesian Filtering: From Kalman Filters to Particle Filters and Beyond,” Technical Report (Adaptive Systems Laboratory, McMaster University, Hamilton, Ontario, Canada, 2003) 38 pp.Google Scholar
25.Syed, Z. F., Aggarwal, P., Goodall, C., Niu, X. and El-Sheimy, N., “A new multi-position calibration method for MEMS inertial navigation systems,” Meas. Sci. Technol. 18, 1897–1907 (2007).CrossRefGoogle Scholar