Skip to main content
SpringerLink
Log in

Fundamental Limits of Self-localization for Cooperative Robotic Platforms Using Signals of Opportunity

  • Chapter
  • First Online:
Cooperative Robots and Sensor Networks 2015

Part of the book series: Studies in Computational Intelligence ((SCI,volume 604))

Abstract

A fundamental problem in robotic applications is the localization of the robots. We consider the problem of global self-localization for a robotic platform with autonomous robots using signals of opportunity (SOOP). We first give a brief overview of the state-of-the-art in robotic localization using SOOP, and then propose a scheme that requires minimal prior environmental information, no pre-configuration, and only loose synchronization between the robots. To further analyze the potential for the use of SOOP in robotic localization and to investigate the effect of clock asynchronism, we derive an analytical expression for the equivalent Fisher information matrix of the Cramér-Rao lower bound (CRLB). The derivation is based on the received signal waveform, and allows us to analyze the contributions of various factors to the localization accuracy. The CRLB provides a valuable guideline for the design of a robotic platform in which a desired level of localization accuracy is to be achieved. We also analyze the distortions in the time difference of arrival and frequency difference of arrival measurements caused by different clock offsets and skews at the robots. We propose a robust algorithm to estimate robot location and velocity, which mitigates the clock biases. Simulation results suggest that our proposed algorithm approaches the CRLB when clock skews have small standard deviations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The notation \(\mathbb {E}_{y|x}\) means taking the expectation over y conditioned on x, while \(p \left( \left. y \right| x \right) \) is the probability density function of y conditioned on x.

References

  1. Cox, I.J.: Blanche: Position estimation for an autonomous robot vehicle. In: IEEE/RSJ International Workshop on Intelligent Robots and Systems, Sept 1989, pp. 432–439

    Google Scholar 

  2. Win, M.Z., Conti, A., Mazuelas, S., Shen, Y., Gifford, W.M., Dardari, D., Chiani, M.: Network localization and navigation via cooperation. IEEE Commun. Mag. 49(5), 56–62 (2011)

    Article  Google Scholar 

  3. Borenstein, J., Everett, H.R., Feng, L., Wehe, D.: Mobile robot positioning—sensors and techniques. J. Rob. Syst. 14(4), 231–249 (1997)

    Article  Google Scholar 

  4. Borenstein, J., Feng, L., Borenstein, C.J.: Measurement and correction of systematic odometry errors in mobile robots. IEEE Trans. Rob. Autom. 12(6), 869–880 (1996)

    Article  Google Scholar 

  5. Leonard, J., Durrant-Whyte, H.: Mobile robot localization by tracking geometric beacons. IEEE Trans. Rob. Autom. 7(3), 376–382 (1991). Jun

    Article  Google Scholar 

  6. Basic Guide to Advanced Navigation, NATO Science and Technology Organization, Feb 2010

    Google Scholar 

  7. Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: 6th ACM International Conference on Mobile Computing and Networking (ACM MOBICOM), Aug 2000, Boston

    Google Scholar 

  8. Oman, J.: Crafting a cricket batsman robot, Master Thesis, Lulea University of Technology, 2005

    Google Scholar 

  9. Priyantha, N.B.: The cricket indoor location system, Master’s Thesis, Massachusetts Institute of Technology, 2005

    Google Scholar 

  10. Harter, A., Hopper, A., Steggles, P., Ward, A., Webster, P.: The anatomy of a context-aware application. Wirel. Netw. 8(2–3), 187–197 (2002)

    Article  MATH  Google Scholar 

  11. Fukuju, Y., Minami, M., Morikawa, H., Aoyama, T.: DOLPHIN: an autonomous indoor positioning system in ubiquitous computing environment. In: IEEE Workshop on Software Technologies for Future Embedded Systems, May 2003, pp. 53–56

    Google Scholar 

  12. Minami, M., Fukuju, Y., Hirasawa, K., Yokoyama, S., Mizumachi, M., Morikawa, H., Aoyama, T.: DOLPHIN: a practical approach for implementing a fully distributed indoor ultrasonic positioning systems. In: Davies, N., Mynatt, E., Siio, I. (eds.) UbiComp 2004: Ubiquitous Computing, ser. Lecture Notes in Computer Science, Vol. 3205, pp. 347–365. Springer, Berlin, Heidelberg (2004)

    Google Scholar 

  13. Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking systems. In: IEEE International Conference on Computer Communications (INFOCOMM’00), 2000, pp. 775–784

    Google Scholar 

  14. Want, R., Hopper, A., Falcão, V., Gibbons, J.: The active badge location system. ACM Trans. Inf. Syst. 10(1), 91–102 (1992)

    Article  Google Scholar 

  15. Ubisense Real-Time Location Fact Sheet. http://www.ubisense.net. Accessed 2 Mar 2010

  16. Merry, L.A., Faragher, R.M., Scheding, S.: Comparison of opportunistic signals for loclisation. In: 7th IFAC Symposium on Intelligent Autonomous Vehicles (2010), vol. 7, no. 1, Lecce, Italy

    Google Scholar 

  17. Parker, L.: EnglishCurrent state of the art in distributed autonomous mobile robotics. In: Parker, L., Bekey, G., Barhen, J. (eds.) EnglishDistributed Autonomous Robotic Systems, Vol. 4, pp. 3–12. Springer, Japan (2000)

    Google Scholar 

  18. Kurazume, R., Nagata, S., Hirose, S.: Cooperative positioning with multiple robots. In: IEEE International Conference on Robotics and Automation, 1994, vol. 2, pp. 1250–1257

    Google Scholar 

  19. Kurazume, R., Hirose, S.: English an experimental study of a cooperative positioning system. Engl. Auton. Robots 8(1), 43–52 (2000)

    Google Scholar 

  20. Grabowski, R., Navarro-Serment, L., Paredis, C., Khosla, P.: Heterogeneous teams of modular robots for mapping and exploration. Engl. Auton. Robots 8(3), 293–308 (2000)

    Google Scholar 

  21. Rekleitis, I., Dudek, G., Milios, E.: English Multi-robot collaboration for robust exploration. Engl. Ann. Math. Artif. Intell. 31(1–4), 7–40 (2001)

    Google Scholar 

  22. Rekleitis, I., Dudek, G., Milios, E.: Probabilistic cooperative localization and mapping in practice. In: IEEE International Conference on Robotics and Automation (ICRA’03), Sept 2003, vol. 2, pp. 1907–1912

    Google Scholar 

  23. Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Engl. Auton. Robots 8(3), 325–344 (2000)

    Google Scholar 

  24. Caglioti, V., Citterio, A., Fossati, A.: Cooperative, distributed localization in multi-robot systems: a minimum-entropy approach. In: IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS’06), June 2006, pp. 25–30

    Google Scholar 

  25. Roumeliotis, S., Bekey, G.A.: Collective localization: a distributed kalman filter approach to localization of groups of mobile robots. In: IEEE International Conference on Robotics and Automation (ICRA’00), 2000, vol. 3, pp. 2958–2965

    Google Scholar 

  26. Roumeliotis, S., Bekey, G.A.: Distributed multirobot localization. IEEE Trans. Robot. Autom. 18(5), 781–795 (2002)

    Google Scholar 

  27. Martinelli, A.: Improving the precision on multi robot localization by using a series of filters hierarchically distributed. In: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, Oct 2007, pp. 1053–1058

    Google Scholar 

  28. Howard, A., Matark, M., Sukhatme, G.: Localization for mobile robot teams using maximum likelihood estimation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, vol. 1, pp. 434–439

    Google Scholar 

  29. Howard, A., Mataric, M., Sukhatme, G.: Localization for mobile robot teams: a distributed MLE approach. In: Siciliano, B., Dario, P. (eds.) English Experimental Robotics VIII, ser. Springer Tracts in Advanced Robotics, Springer, Berlin, Heidelberg, 2003, vol. 5, pp. 146–155

    Google Scholar 

  30. Nerurkar, E.D., Roumeliotis, S., Martinelli, A.: Distributed maximum a posteriori estimation for multi-robot cooperative localization. In: IEEE International Conference on Robotics and Automation (ICRA ’09), May 2009, pp. 1402–1409

    Google Scholar 

  31. Trawny, N., Roumeliotis, S., Giannakis, G.: Cooperative multi-robot localization under communication constraints. In: IEEE International Conference on Robotics and Automation (ICRA ’09), May 2009, pp. 4394–4400

    Google Scholar 

  32. Roumeliotis, S., Rekleitis, I.: Propagation of uncertainty in cooperative multirobot localization: analysis and experimental results. Engl. Auton. Robots 17(1), 41–54 (2004)

    Google Scholar 

  33. Mourikis, A., Roumeliotis, S.: Performance analysis of multirobot cooperative localization. IEEE Trans. Robot. 22(4), 666–681 (2006)

    Article  Google Scholar 

  34. Howard, A., Siddiqi, S., Sukhatme, G.S.: An experimental study of localization using wireless ethernet. In: 4th International Conference on Field and Service Robotics, 2003

    Google Scholar 

  35. Ladd, A.M., Bekris, K.E., Rudys, A., Kavraki, L.E., Wallach, D.S.: Robotics-based location sensing using wireless ethernet. Wirel. Netw. 11, 189–204 (2005)

    Article  Google Scholar 

  36. Huang, J., Millman, D., Quigley, M., Stavens, D., Thrun, S., Aggarwal, A.: Efficient, generalized indoor WiFi graphSLAM. In: IEEE International Conference on Robotics and Automation (ICRA’11), May 2011, pp. 1038–1043

    Google Scholar 

  37. Li, B., Gallagher, T., Dempster, A., Rizos, C.: How feasible is the use of magnetic field alone for indoor positioning? In: International Conference on Indoor Positioning and Indoor Navigation (IPIN’12), Nov 2012, pp. 1–9

    Google Scholar 

  38. Jimenez, A., Zampella, F., Seco, F.: Light-matching: a new signal of opportunity for pedestrian indoor navigation. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN’13), Oct 2013, pp. 1–10

    Google Scholar 

  39. Schwaighofer, A., Grigoras, M., Tresp, V., Hoffmann, C.: GPPS: a Gaussian process positioning system for cellular networks. In: IN NIPS. MIT Press, 2004

    Google Scholar 

  40. Del Peral-Rosado, J., Lopez-Salcedo, J., Seco-Granados, G., Zanier, F., Crisci, M.: Achievable localization accuracy of the positioning reference signal of 3GPP LTE. In: International Conference on Localization and GNSS (ICL-GNSS’12), June 2012, pp. 1–6

    Google Scholar 

  41. Dammann, A., Sand, S., Raulefs, R.: Signals of opportunity in mobile radio positioning. In: Proceedings of the 20th European Signal Processing Conference, Aug 2012, pp. 549–553

    Google Scholar 

  42. Moghtadaiee, V., Dempster, A., Lim, S.: Indoor localization using FM radio signals: a fingerprinting approach. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN’11), Sept 2011, pp. 1–7

    Google Scholar 

  43. Dong, Q., Dargie, W.: Evaluation of the reliability of RSSI for indoor localization. In: International Conference on Wireless Communications in Unusual and Confined Areas (ICWCUCA’12), Aug 2012, pp. 1–6

    Google Scholar 

  44. LaMarca, A., Hightower, J., Smith, I., Consolvo, S.: Self-mapping in 802.11 location systems. In: Beigl, M., Intille, S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005: Ubiquitous Computing, ser. Lecture Notes in Computer Science, 2005, vol. 3660, pp. 87–104. Springer, Berlin, Heidelberg

    Google Scholar 

  45. Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., Kavraki, L.E.: Practical robust localization over large-scale 802.11 wireless networks. In: Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MobiCom’04), 2004, pp. 70–84. ACM, New York

    Google Scholar 

  46. Ferris, B., Haehnel, D., Fox, D.: Gaussian processes for signal strength-based location estimation. In: Proceedings of Robotics: Science and Systems, Philadelphia, Aug 2006

    Google Scholar 

  47. Ferris, B., Fox, D., Lawrence, N.: WiFi-SLAM using Gaussian process latent variable models. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, San Francisco, 2007, pp. 2480–2485

    Google Scholar 

  48. Lim, H., Kung, L.-C., Hou, J.C., Luo, H.: Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure. Wirel. Netw. 16(2), 405–420 (2010)

    Article  Google Scholar 

  49. Gallagher, T., Li, B., Dempster, A., Rizos, C.: A sector-based campus-wide indoor positioning system. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN’10), Sept 2010, pp. 1–8

    Google Scholar 

  50. Li, B., Wang, Y., Lee, H., Dempster, A., Rizos, C.: Method for yielding a database of location fingerprints in WLAN. IEE Proc. Commun. 152(5), 580–586 (2005). Oct

    Google Scholar 

  51. Leng, M., Wu, Y.-C.: Distributed clock synchronization for wireless sensor networks using belief propagation. IEEE Trans. Signal Process. 59(11), 5404–5414 (2011)

    Article  MathSciNet  Google Scholar 

  52. Leng, M., Tay, W.P., Quek, T.Q.S.: Cooperative and distributed localization for wireless sensor networks in multipath environments. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 3125–3128

    Google Scholar 

  53. Leng, M., Tay, W.P., See, C., Razul, S., Win, M.: Modified CRLB for cooperative geolocation of two devices using signals of opportunity. IEEE Trans. Wirel. Commun. 13(7), 3636–3649 (2014)

    Article  Google Scholar 

  54. Sun, M., Ho, K.C.: An asymptotically efficient estimator for TDOA and FDOA positioning of multiple disjoint sources in the presence of sensor location uncertainties. IEEE Trans. Signal Process. 59(7), 3434–3440 (2011)

    Article  MathSciNet  Google Scholar 

  55. Musicki, D., Kaune, R., Koch, W.: Mobile emitter geolocation and tracking using TDOA and FDOA measurements. IEEE Trans. Signal Process. 58(3), 1863–1874 (2010)

    Article  MathSciNet  Google Scholar 

  56. Wei, H.-W., Peng, R., Wan, Q., Chen, Z.-X., Ye, S.-F.: Multidimensional scaling analysis for passive moving target localization with TDOA and FDOA measurements. IEEE Trans. Signal Process. 58(3), 1677–1688 (2010)

    Article  MathSciNet  Google Scholar 

  57. Stein, S.: Algorithms for ambiguity function processing. IEEE Trans. Acoust., Speech, Signal Process. 29(3), 588–599 (1981)

    Article  Google Scholar 

  58. Kay, S.M.: Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  59. Harry, K.L.B., Van Trees, L. (ed.): Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. Wiley-IEEE Press, New York (2007)

    Google Scholar 

  60. Moeneclaey, M.: On the true and the modified Cramer-Rao bounds for the estimation of a scalar parameter in the presence of nuisance parameters. IEEE Trans. Commun. 46(11), 1536–1544 (1998)

    Article  Google Scholar 

  61. Gini, F., Reggiannini, R., Mengali, U.: The modified Cramer-Rao bound in vector parameter estimation. IEEE Trans. Commun. 46(1), 52–60 (1998)

    Article  Google Scholar 

  62. Hlawatsch, F., Auger, F. (eds.) Time-Frequency Analysis: Concepts and Methods. Wiley, London (2008)

    Google Scholar 

  63. Shen, Y., Win, M.: Fundamental limits of wideband localization—part I: a general framework. IEEE Trans. Inf. Theory 56(10), 4956–4980 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Mei Leng & Wee Peng Tay

Authors
  1. Mei Leng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wee Peng Tay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mei Leng .

Editor information

Editors and Affiliations

  1. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia, and CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, Porto, Portugal

    Anis Koubâa

  2. Depto. Ing. de Sistemas y Automática Escuela Superior de Ingenieros, University of Seville, Sevilla, Spain

    J.Ramiro Martínez-de Dios

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Leng, M., Tay, W.P. (2015). Fundamental Limits of Self-localization for Cooperative Robotic Platforms Using Signals of Opportunity. In: Koubâa, A., Martínez-de Dios, J. (eds) Cooperative Robots and Sensor Networks 2015. Studies in Computational Intelligence, vol 604. Springer, Cham. https://doi.org/10.1007/978-3-319-18299-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18299-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18298-8

  • Online ISBN: 978-3-319-18299-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation