Skip to main content
SpringerLink
Log in

Autonomous Navigation for Mobile Robots with Human-Robot Interaction

  • Chapter
Robot Intelligence

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

Dynamic and complex indoor environments present a challenge for mobile robot navigation. The robot must be able to simultaneously map the environment, which often has repetitive features, whilst keep track of its pose and location. This chapter introduces some of the key considerations for human guided navigation. Rather than letting the robot explore the environment fully autonomously, we consider the use of human guidance for progressively building up the environment map and establishing scene association, learning, as well as navigation and planning. After the guide has taken the robot through the environment and indicated the points of interest via hand gestures, the robot is then able to use the geometric map and scene descriptors captured during the tour to create a high-level plan for subsequent autonomous navigation within the environment. Issues related to gesture recognition, multi-cue integration, tracking, target pursuing, scene association and navigation planning are discussed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

References

  1. Ascani, A., Frontoni, E., Mancini, A., Zingaretti, P.: Feature group matching for appearance-based localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3933–3938 (2008)

    Google Scholar 

  2. Bauer, A., Klasing, K., Lidoris, G., Mühlbauer, Q., Rohrmüller, F., Sosnowski, S., Xu, T., Kühnlenz, K., Wollherr, D., Buss, M.: The autonomous city explorer: towards natural human-robot interaction in urban environments. Int. J. Soc. Robot. 1(2), 127–140 (2009)

    Article  Google Scholar 

  3. Bellotto, N., Hu, H.: Multisensor integration for human-robot interaction. IEEE J. Intell. Cybern. Syst. 1 (2005)

    Google Scholar 

  4. Bellotto, N., Hu, H.: Multisensor-based human detection and tracking for mobile service robots. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 39(1), 167–181 (2009)

    Article  Google Scholar 

  5. Buhmann, J., Burgard, W., Cremers, A., Fox, D., Hofmann, T., Schneider, F., Strikos, J., Thrun, S.: The mobile robot Rhino. AI Mag. 16(2), 31 (1995)

    Google Scholar 

  6. Busch, M., Blackman, S.: Evaluation of IMM filtering for an air defense system application. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 2561, pp. 435–447 (1995)

    Google Scholar 

  7. Chakravarty, P., Jarvis, R.: Panoramic vision and laser range finder fusion for multiple person tracking. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2949–2954 (2006)

    Google Scholar 

  8. Fritsch, J., Kleinehagenbrock, M., Lang, S., Plötz, T., Fink, G., Sagerer, G.: Multi-modal anchoring for human–robot interaction. Robot. Auton. Syst. 43(2–3), 133–147 (2003)

    Article  Google Scholar 

  9. Hasanuzzaman, M., Ampornaramveth, V., Zhang, T., Bhuiyan, M., Shirai, Y., Ueno, H.: Real-time vision-based gesture recognition for human robot interaction. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 413–418 (2004)

    Google Scholar 

  10. Ishii, Y., Hongo, H., Yamamoto, K., Niwa, Y.: Real-time face and head detection using four directional features. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 403–408 (2004)

    Google Scholar 

  11. Jin, Y., Mokhtarian, F.: Towards robust head tracking by particles. In: IEEE International Conference on Image Processing (ICIP), vol. 3, pp. 864–867 (2005)

    Google Scholar 

  12. Koenig, N.: Toward real-time human detection and tracking in diverse environments. In: IEEE 6th International Conference on Development and Learning (ICDL), pp. 94–98 (2007)

    Google Scholar 

  13. Krotosky, S., Cheng, S., Trivedi, M.: Real-time stereo-based head detection using size, shape and disparity constraints. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 550–556 (2005)

    Google Scholar 

  14. Lee, H., Kim, J.: An HMM-based threshold model approach for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(10), 961–973 (1999)

    Article  Google Scholar 

  15. Li, F., Kosecka, J.: Probabilistic location recognition using reduced feature set. In: IEEE International Conference on Robotics and Automation, pp. 3405–3410. Citeseer (2006)

    Google Scholar 

  16. Li, Y., Ai, H., Huang, C., Lao, S.: Robust head tracking based on a multi-state particle filter. In: 7th International Conference on Automatic Face and Gesture Recognition, pp. 335–340 (2006)

    Google Scholar 

  17. Lindstrom, M., Eklundh, J.: Detecting and tracking moving objects from a mobile platform using a laser range scanner. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 1364–1369 (2001)

    Google Scholar 

  18. Loper, M., Koenig, N., Chernova, S., Jones, C., Jenkins, O.: Mobile human-robot teaming with environmental tolerance. In: Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, pp. 157–164. ACM, New York (2009)

    Chapter  Google Scholar 

  19. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  20. Luo, J., Savakis, A., Singhal, A.: A Bayesian network-based framework for semantic image understanding. Pattern Recognit. 38(6), 919–934 (2005)

    Article  Google Scholar 

  21. Meyer, J., Filliat, D.: Map-based navigation in mobile robots, II: a review of map-learning and path-planning strategies. Cogn. Syst. Res. 4(4), 283–317 (2003)

    Article  Google Scholar 

  22. Montemerlo, M., Roy, N., Thrun, S.: Perspectives on standardization in mobile robot programming: the Carnegie Mellon navigation (CARMEN) toolkit. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2436–2441. Citeseer (2003)

    Google Scholar 

  23. Nickel, K., Stiefelhagen, R.: Real-Time Person Tracking and Pointing Gesture Recognition for Human-Robot Interaction. In: Proceedings of the Computer Vision in Human-Computer Interaction: ECCV 2004 Workshop on HCI, Prague, Czech Republic, p. 28. Springer, New York (2004)

    Google Scholar 

  24. Reynolds, C.: Steering behaviors for autonomous characters. In: Game Developers Conference. http://www.red3d.com/cwr/steer/gdc99 (1999)

  25. Schaffalitzky, F., Zisserman, A.: Automated scene matching in movies. In: Lecture Notes in Computer Science, pp. 186–197 (2002)

    Google Scholar 

  26. Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 530–535 (1997)

    Article  Google Scholar 

  27. Shalom, Y., Blair, W.: Multitarget-Multisensor Tracking: Applications and Advances, vol. 3. Artech House, Boston (2000)

    Google Scholar 

  28. Shan, C., Wei, Y., Tan, T., Ojardias, F.: Real time hand tracking by combining particle filtering and mean shift. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 669–674 (2004)

    Google Scholar 

  29. Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600. Citeseer (1994)

    Google Scholar 

  30. Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings of the ICCV, vol. 2, pp. 1470–1477. Citeseer (2003)

    Google Scholar 

  31. Thrun, S.: Exploration and model building in mobile robot domains. In: Proceedings of the IEEE International Conference on Neural Networks vol. 1, pp. 175–180. Citeseer (1993)

    Google Scholar 

  32. Thrun, S., Bücken, S.: Learning maps for indoor mobile robot navigation. Carnegie Mellon University, technical report No. CMU-CS-96-121 (1996)

    Google Scholar 

  33. Topp, E., Christensen, H.: Tracking for following and passing persons. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 70–76. Citeseer (2005)

    Google Scholar 

  34. Ullah, M., Pronobis, A., Caputo, B., Luo, J., Jensfelt, P., Christensen, H.: Towards robust place recognition for robot localization. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 530–537. Citeseer (2008)

    Google Scholar 

  35. Valibeik, S., Yang, G.: Segmentation and tracking for vision based human robot interaction. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 471–476. IEEE Computer Society, Washington (2008)

    Chapter  Google Scholar 

  36. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  37. Wimpey, B., Drucker, E., Martin, M., Potter, W.: A multilayered approach to location recognition. In: Proceedings of the SoutheastCon, pp. 1–7

    Google Scholar 

  38. Won, W., Kim, M., Son, J.: Driver’s head detection model in color image for driver’s status monitoring. In: 11th International IEEE Conference on Intelligent Transportation Systems, pp. 1161–1166 (2008)

    Google Scholar 

  39. Zhang, Q., Pless, R.: Extrinsic calibration of a camera and laser range finder (improves camera calibration). In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Proceedings, vol. 3, pp. 2301–2306 (2004)

    Google Scholar 

  40. Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: International Conference on Computer Vision vol. 1, pp. 666–673 (1999)

    Google Scholar 

  41. Zhang, Z., Gunes, H., Piccardi, M.: An accurate algorithm for head detection based on XYZ and HSV hair and skin color models. In: 15th IEEE International Conference on Image Processing (ICIP), pp. 1644–1647 (2008)

    Google Scholar 

  42. Zhu, Y., Ren, H., Xu, G., Lin, X.: Toward real-time human-computer interaction with continuous dynamic hand gestures. In: Proceedings of the Fourth International Conference on Automation Face Gesture Recognition, Grenoble, France, pp. 544–549. IEEE Computer Society, Los Alamitos (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Biomedical Engineering, Imperial College of London, London, UK

    James Ballantyne, Edward Johns, Salman Valibeik, Charence Wong & Guang-Zhong Yang

Authors
  1. James Ballantyne
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edward Johns
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salman Valibeik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Charence Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guang-Zhong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James Ballantyne .

Editor information

Editors and Affiliations

  1. School of Creative Technologies, The University of Portsmouth, Portsmouth, PO1 2DJ, United Kingdom

    Honghai Liu

  2. Department of Computer Science, University of Essex, Wivenhoe Park, United Kingdom

    Dongbing Gu

  3. , School of Engineering, University of Brighton, Moulsecoomb, Brighton, BN2 4GJ, United Kingdom

    Robert J. Howlett

  4. Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB, United Kingdom

    Yonghuai Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag London Limited

About this chapter

Cite this chapter

Ballantyne, J., Johns, E., Valibeik, S., Wong, C., Yang, GZ. (2010). Autonomous Navigation for Mobile Robots with Human-Robot Interaction. In: Liu, H., Gu, D., Howlett, R., Liu, Y. (eds) Robot Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-329-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-84996-329-9_12

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-328-2

  • Online ISBN: 978-1-84996-329-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation