Skip to main content
SpringerLink
Log in

Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

An innovative neuro-evolutionary approach for mobile robot egomotion estimation with a 3D ToF camera is proposed. The system is composed of two main modules following a preprocessing step. The first module is a Neural Gas network that computes a Vector Quantization of the preprocessed camera 3D point cloud. The second module is an Evolution Strategy that estimates the robot motion parameters by performing a registration process, searching on the space of linear transformations, restricted to the translation and rotation, between the codebooks obtained for successive camera readings. The fitness function is the matching error between the predicted and the observed codebook corresponding to the next camera readings. In this paper, we report results of an implementation of this system tested on data from a real mobile robot, and provide several comparisons between our and other well-known registration algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Those recorded datasets are available in our web site: http://www.ehu.es/ccwintco/index.php/Conjuntos_de_datos_3D.

References

  1. Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517

    Article  MATH  MathSciNet  Google Scholar 

  2. Besl PJ, McKay HD (1992) A method for registration of 3-D shapes. Pattern Anal Mach Intell IEEE Trans 14(2):239–256

    Article  Google Scholar 

  3. Beyer H-G, Schwefel H-P (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52

    Article  MATH  MathSciNet  Google Scholar 

  4. Chow CK, Tsui HT, Lee T (2004) Surface registration using a dynamic genetic algorithm. Pattern Recogn 37(1):105–117

    Article  MATH  Google Scholar 

  5. Dissanayake G (2001) A solution to the simultaneous localization and map building (SLAM) problem. Robot Autom IEEE Trans 17(3):229–241

    Article  Google Scholar 

  6. Guðmundsson SA, Larsen R, Ersbøll BK (2007) Robust pose estimation using the SwissRanger SR-3000 camera. In: Image analysis, volume 4522/2007 of lecture notes in computer science. Springer, pp 968–975

  7. Kohonen T (2001) Self-organizing maps. Springer, Berlin

    MATH  Google Scholar 

  8. Lange R, Seitz P (2001) Solid-state time-of-flight range camera. IEEE J Quantum Electron 37(3):390–397

    Article  Google Scholar 

  9. Martinetz TM, Berkovich SG, Schulten KJ (1993) ‘Neural-Gas’ network for vector quantization and its application to time-series prediction. Neural Netw IEEE Trans 4(4):558–569

    Article  Google Scholar 

  10. Martinetz TM, Schulten KJ (1991) In: Proc international conference on artificial neural networks, chapter a neural-gas network learns topologies. North-Holland, Amsterdam, pp 397–402

  11. Oggier T, Lehmann M, Kaufmannn R, Schweizer M, Richter M, Metzler P, Lang G, Lustenberger F, Blanc N (2003) An all-solid-state optical range camera for 3D-real-time imaging with sub-centimeter depth-resolution (SwissRanger). In: Proc SPIE, vol 5249. pp 634–545

  12. Randy LH, Sue EH (2004) Practical genetic algorithms, 2nd edn. Wiley, Hoboken

    MATH  Google Scholar 

  13. Salvi J, Matabosch C, Fofi D, Forest J (2007) A review of recent range image registration methods with accuracy evaluation. Image Vis Comput 25(5):578–596

    Article  Google Scholar 

  14. Thrun S (2002) Exploring artificial intelligence in the new millenium, chapter robotic mapping: a survey. Morgan Kaufmann, San Mateo

    Google Scholar 

  15. Vesanto J, Alhoniemi E, Himberg J, Kiviluoto K, Parviainen J (1999) Self-organizing map for data mining in matlab: the SOM toolbox. Simulation News Eur 25(54):54. http://www.cis.hut.fi/projects/somtoolbox/

  16. Villaverde I, Echegoyen Z, Graña M (2009) Neuro-evolutive system for ego-motion estimation with a 3D camera. In: Köppen M (ed) Advances in neuro-information processing, vol 5506/2009 of lecture notes in computer sciences. Springer, pp 1021–1028

  17. Villaverde I, Graña M (2008) A hybrid intelligent system for robot ego-motion estimation with a 3D camera. In: Corchado E, Abraham A, Pedrycz W (eds) Hybrid artificial intelligence systems, vol 5271 of lecture notes in artificial intelligence. Springer, pp 657–664

  18. Ye C, Hegde G-PM (2009) Robust edge extraction for SwissRanger SR-3000 range images. In: Robotics and automation, 2009. ICRA ’09. IEEE international conference on, May, pp 2437–2442

  19. Zinsser T, Schnidt H, Niermann J (2003) A refined ICP algorithm for robust 3-D correspondences estimation. In: International conference on image processing pp 695–698

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country, Pº Manuel de Lardizabal, 1, 20018, San Sebastian, Spain

    Ivan Villaverde & Manuel Graña

Authors
  1. Ivan Villaverde
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ivan Villaverde.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Villaverde, I., Graña, M. Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera. Neural Comput & Applic 20, 345–354 (2011). https://doi.org/10.1007/s00521-010-0384-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0384-6

Keywords

Search

Navigation