Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-18T08:30:45.997Z Has data issue: false hasContentIssue false
  • English
  • Français

Information Fusion of GPS, INS and Odometer Sensors for Improving Localization Accuracy of Mobile Robots in Indoor and Outdoor Applications

Published online by Cambridge University Press:  27 May 2020

Sofia Yousuf  and
Muhammad Bilal Kadri Open the ORCID record for Muhammad Bilal Kadri [Opens in a new window]
Sofia Yousuf
Affiliation:
Department of Mechatronics, College of Engineering, Karachi Institute of Economics and Technology (PAF-KIET), Karachi, Pakistan. E-mail: sofia.yousuf@pafkiet.edu.pk
Muhammad Bilal Kadri*
Affiliation:
Department of Mechatronics, College of Engineering, Karachi Institute of Economics and Technology (PAF-KIET), Karachi, Pakistan. E-mail: sofia.yousuf@pafkiet.edu.pk
*
*Corresponding author. E-mail: bilal.kadri@pafkiet.edu.pk
Get access Rights & Permissions [Opens in a new window]

Summary

In mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

Keywords

Mobile robotsMotion planningRobot localizationPose estimation and registrationSensor or actuator design
Type
Articles
Information
Robotica , Volume 39 , Issue 2 , February 2021 , pp. 250 - 276
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hoshino, S. and Maki, K., “Safe and efficient motion planning of MMRs based on artificial potential for human behavior and robot congestion,” Adv. Robot. 29(17), 1095–1109 (2015). ISSN 0169-1864.CrossRefGoogle Scholar
Bogue, R., “Robots for space exploration,” Ind. Robot Int. J. 39(4), 323–328 (2012). ISSN 0143-991X.CrossRefGoogle Scholar
Cassenti, D. N., “A robotics operator manager role for military application,” J. Def. Model. Simul. 13(2), 227–237 (2016). ISSN 1548-5129.Google Scholar
Beasley, R. A., “Medical robots: Current systems and research directions,” J. Robot. 2012 (2012). ISSN 1687-9600.CrossRefGoogle Scholar
He, Y. and Chen, S., “Advances in sensing and processing methods for three-dimensional robot vision,” Int. J. Adv. Robot. Syst. 15(2) (2018). ISSN 1729-8814. https://doi.org/10.1177/1729881418760623.CrossRefGoogle Scholar
Bogue, R., “Domestic robots: Has their time finally come?” Ind. Robot Int. J. 44(2), 129–136 (2017). ISSN 0143-991X.Google Scholar
Nagla, K. S., Uddin, M. and Singh, D., “Multisensor data fusion and integration for mobile robots: A review,” IAES Int. J. Robot. Automat. 3(2), 131 (2014). ISSN 2089-4856.Google Scholar
Sun, K., Mohta, K., Pfrommer, B., Watterson, M., Liu, S., Mulgaonkar, Y., Taylor, C. J. and Kumar, V., “Robust stereo visual inertial odometry for fast autonomous flight,” IEEE Robot. Autom. Lett. 3(2), 965–972 (2018). ISSN 2377-3766.CrossRefGoogle Scholar
Tanveer, F. and Kadri, M. B., “A Simulation Framework for Decentralized Formation Control of Non-Holonomic Differential Drive Robots,” In: 2018 SICE International Symposium on Control Systems (SICE ISCS), 9–11 March 2018, Tokyo, Japan (2018).Google Scholar
Azhar, A. B. and Kadri, M. B., “Empirical Evaluation of Formation Control Scheme Based on Artificial Potential Fields for a Team of Non-Holonomic Mobile Robots,” In: 14th IEEE International Conference on Emerging Technologies (ICET 2018), 21–22 November, 2018, Islamabad, Pakistan (2018).Google Scholar
Wen, S., Zhang, Z., Ma, C., Wang, Y. and Wang, H., “An extended Kalman filter-simultaneous localization and mapping method with Harris-scale-invariant feature transform feature recognition and laser mapping for humanoid robot navigation in unknown environment,” Int. J. Adv. Robot. Syst. 14(6) (2017). ISSN 1729-8814. https://doi.org/10.1177/1729881417744747.Google Scholar
Ma, Y., Chuang, L. S., Bo, W., Zhang, S., Li, M. and Song, P., “Weighted total least squares for the visual localization of a planetary rover,” Photogramm. Eng. Rem. S 84, 605–618 (2018), https://doi.org/10.14358/PERS.84.10.605.Google Scholar
Lu, Y., Xue, Z., Xia, G.-S. and Zhang, L., “A survey on vision-based UAV navigation,” Geo Spatial Inform. Sci., 1–12 (2018). ISSN 1009-5020.Google Scholar
Jean, J.-H., Liu, B.-S., Chang, P.-Z. and Kuo, L.-C., “Attitude detection and localization for unmanned aerial vehicles,” Smart Sci. 4(4), 196–202 (2016). ISSN 2308-0477.Google Scholar
Mikulov, Z., Ducho, F., Dekan, M. and Babinec, A., “Localization of mobile robot using visual system,” Int. J. Adv. Robot. Syst. 14(5) (2017). ISSN 1729-8814. https://doi.org/10.1177/1729881417736085.Google Scholar
Sun, Q., Tian, Y. and Diao, M., “Cooperative localization algorithm based on hybrid topology architecture for multiple mobile robot system,” IEEE Internet Things J. (2018). ISSN 2327-4662.Google Scholar
Sun, Q., Diao, M., Zhang, Y. and Li, Y., “Cooperative localization algorithm for multiple mobile robot system in indoor environment based on variance component estimation,” Symmetry 9(6), 94 (2017).Google Scholar
Kolanowski, K., Wietlicka, A., Kapela, R., Pochmara, J. and Rybarczyk, A., “Multisensor data fusion using Elman Neural Networks,” Appl. Math. Comput. 319, 236–244 (2018). ISSN 0096-3003.Google Scholar
Musavi, N. and Keighobadi, J., “Adaptive fuzzy neuro-observer applied to low cost INS/GPS,” Appl. Soft Comput. 29, 82–94 (2015). ISSN 1568-4946.CrossRefGoogle Scholar
Yang, D., Sun, D., Liu, Y. and Liao, S., “Sensor to sensor calibration of the integrated INS/vision navigation system: Time-domain optimization,” Int. J. Adv. Robot. Syst. 14(3) (2017). ISSN 1729-8814. https://doi.org/10.1177/1729881417707322.CrossRefGoogle Scholar
Jin, X.-B., Su, T.-L., Kong, J.-L., Bai, Y.-T., Miao, B.-B. and Dou, C., “State-of-the-art mobile intelligence: Enabling robots to move like humans by estimating mobility with artificial intelligence,” Appl. Sci. 8(3), 379 (2018).Google Scholar
Sanchez-Lopez, J. L., Arellano-Quintana, V., Tognon, M., Campoy, P. and Franchi, A., “Visual Marker Based Multi-Sensor Fusion State Estimation,” In: IFAC World Congress, 6p.Google Scholar
Li, J., Song, N., Yang, G., Li, M., Cai, Q., “Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm,” Inform. Fusion. 35, 1–10 (2017). ISSN 1566-2535.CrossRefGoogle Scholar
Ryu, J. H., Gankhuyag, G. and Chong, K. T., “Navigation system heading and position accuracy improvement through GPS and INS data fusion,” J. Sens. 2016 (2016). ISSN 1687-725X.Google Scholar
Chen, W. and Zhang, T., “An indoor mobile robot navigation technique using odometry and electronic compass,” Int. J. Adv. Robot. Syst. 14(3) (2017). ISSN 1729-8814. https://doi.org/10.1177/1729881417711643.Google Scholar
Faisal, M., Alsulaiman, M., Hedjar, R., Mathkour, H., Zuair, M., Altaheri, H., Zakariah, M., Bencherif, M. A. and Mekhtiche, M. A., “Enhancement of mobile robot localization using extended Kalman filter,” Adv. Mech. Eng. 8(11) (2016). ISSN 1687-8140. https://doi.org/10.1177/1687814016680142.CrossRefGoogle Scholar
Cho, B.-S., Moon, W.-S., Seo, W.-J. and Baek, K.-R., “A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding,” J. Mech. Sci. Technol. 25(11), 2907–2917 (2011). ISSN 1738-494X.CrossRefGoogle Scholar
Noureldin, A., El-Shafie, A. and Bayoumi, M., “GPS/INS integration utilizing dynamic neural networks for vehicular navigation,” Inform. Fusion 12(1), 48–57 (2011). ISSN 1566-2535.Google Scholar
Benkouider, S., Lagraa, N., Yagoubi, M. B. and Lakas, A. (2011) Reducing Complexity of GPS/INS Integration Scheme Through Neural Networks. In: 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC) (IEEE, 2013) pp. 53–58. ISBN 1467324809.Google Scholar
Drawil, N. M. and Basir, O., “Intervehicle-communication-assisted localization,” IEEE Trans. Intell. Transp. Syst. 11(3), 678–691 (2010). ISSN 1524-9050.Google Scholar
Nada, D., Bousbia-Salah, M. and Bettayeb, M., “Multi-sensor data fusion for wheelchair position estimation with unscented Kalman filter,” Int. J. Autom. Comput. 15(2), 207–217 (2018). ISSN 1476-8186.Google Scholar
Li, W., Wang, Z., Wei, G., Ma, L., Hu, J. and Ding, D., “A survey on multisensor fusion and consensus filtering for sensor networks,” Discrete Dyn. Nat. Soc. 2015 (2015). ISSN 1026-0226.Google Scholar
Welch, G. and Bishop, G., An Introduction to Kalman Filter. Report (University of North Carolina at Chapel Hill, July 24, 2006, 2006).Google Scholar
Tanveer, F., Kadri, M. B., Jumani, N. and Pirwani, N., “Fuzzy Based Tuning of a Sensor Fusion Based Low Cost Attitude Estimator,” In: The 6th International Conference on Innovative Computing Technology (INTECH 2016), 19–21 September 2016, Islamabad, Pakistan (2016).Google Scholar
Yousuf, S. and Kadri, M. B., “Sensor Fusion of INS, Odometer and GPS for Robot Localization,” In: 2016 IEEE Conference on Systems, Process and Control (ICSPC 2016), 16–18 December 2016 Melaka, Malaysia (2016).Google Scholar
Yousuf, S. and Kadri, M. B., “Robot Localization in Indoor and Outdoor Environments by Multi-Sensor Fusion,” In: 14th IEEE International Conference on Emerging Technologies (ICET 2018), 21–22 November, 2018, Islamabad, Pakistan (2018).Google Scholar
Qazi, S. H., Kadri, M. B., “Revisiting Constraint based geo-location: improving accuracy through removal of outliers,” Int. Arab J. Inform. Technol. (IAJIT) 15(2) (2018).Google Scholar
Luo, R. C., Yih, C.-C. and Su, K. L., “Multisensor fusion and integration: Approaches, applications, and future research directions,” IEEE Sens. J. 2(2), 107–119 (2002). ISSN 1530-437X.Google Scholar
Mehra, R., “On the identification of variances and adaptive Kalman filtering,” IEEE Trans. Autom. Cont. 15(2), 175–184 (1970).CrossRefGoogle Scholar
Paliwal, K. and Basu, A., “A Speech Enhancement Method Based on Kalman filtering,” In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1987, vol. 12 (IEEE, 1987).Google Scholar
Reif, K. and Unbehauen, R., “The extended Kalman filter as an exponential observer for nonlinear systems,” IEEE Trans. Sig. Process. 47(8), 2324–2328 (1999).10.1109/78.774779CrossRefGoogle Scholar
Kim, P., Kalman Filter for Beginners: With MATLAB Examples (CreateSpace, 2011).Google Scholar
Zadeh, L. A., “Fuzzy logic, neural networks, and soft computing,” Commun. ACM 37(3), 77–85 (1994).CrossRefGoogle Scholar