Towards Resolving the Kidnapped Robot Problem: Topological Localization from Crowdsourcing and Georeferenced Images

  • Sotirios DiamantasEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)


In this research, we address the kidnapped robot problem, a fundamental localization problem where a robot has been carried to an arbitrary unknown location for which no prior maps exist. In our approach, topological maps are created using a single sensor, namely, a camera, with the aim to localize the robot and drive it to its initial, home, position. Our approach differs significantly from other approaches that attempt to solve this localization problem. In order to localize a robot within an unknown environment we exploit the potential of social networks and extract the GPS information from in georeferenced images. The experiments carried out within a university campus affirm the validity of our approach and provide the means to resolving similar problems with the methods presented.


Kidnapped robot problem Robot localization Georeferencd images Crowdsourcing 


  1. 1.
    Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artif. Intell. 128(1–2), 99–141 (2000)zbMATHGoogle Scholar
  2. 2.
    Bukhori, I., Ismail, Z.H.: Detection of kidnapped robot problem in monte carlo localization based on the natural displacement of the robot. Int. J. Adv. Robot. Syst. 14(4), 1–6 (2017)CrossRefGoogle Scholar
  3. 3.
    Bukhori, I., Ismail, Z.H., Namerikawa, T.: Detection strategy for kidnapped robot problem in landmark-based map monte carlo localization. In: Proceedings of the IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 1609–1614 (2015)Google Scholar
  4. 4.
    Tian, Y., Ma, S.: Kidnapping detection and recognition in previous unknown environment. J. Sens. 2017, 1–15 (2017)Google Scholar
  5. 5.
    Tian, Y., Ma, S.: Probabilistic double guarantee kidnapping detection in SLAM. Robot. Biomimetics 3(20), 1–7 (2016)Google Scholar
  6. 6.
    Majdik, A., Popa, M., Tamas, L., Szoke, I., Lazea, G.: New approach in solving the kidnapped robot problem. In: Proceedings of the 41st International Symposium in Robotics (ISR) and 6th German Conference on Robotics (ROBOTIK), pp. 1–6 (2010)Google Scholar
  7. 7.
    Wei, L.: A feature-based solution for kidnapped robot problem. Ph.D. thesis, Electrical and Computer Engineering, Auburn University (2015)Google Scholar
  8. 8.
    Seow, Y., Miyagusuku, R., Yamashita, A., Asama, H.: Detecting and solving the kidnapped robot problem using laser range finder and wifi signal. In: Proceedings of the IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 303–308 (2017)Google Scholar
  9. 9.
    Yi, C., Choi, B.U.: Detection and recovery for kidnapped-robot problem using measurement entropy. Grid Distrib. Comput. 261, 293–299 (2011)CrossRefGoogle Scholar
  10. 10.
    Gonzalez-Buesa, C., Campos, J.: Solving the mobile robot localization problem using string matching algorithms. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2475–2480 (2004)Google Scholar
  11. 11.
    Lamon, P., Nourbakhsh, I., Jensen, B., Siegwart, R.: Deriving and matching image fingerprint sequences for mobile robot localization. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1609–1614 (2001)Google Scholar
  12. 12.
    Mavridis, N., Emami, S., Datta, C., Kazmi, W., BenAbdelkader, C., Toulis, P., Tanoto, A., Rabie, T.: FaceBots: steps towards enhanced long-term human-robot interaction by utilizing and publishing online social information. J. Behav. Robot. 1(3), 169–178 (2011)Google Scholar
  13. 13.
    Mavridis, N., Datta, C., Emami, S., Tanoto, A., BenAbdelkader, C., Rabie, T.: FaceBots: robots utilizing and publishing social information in Facebook. In: Proceedings of the IEEE Human-Robot Interaction Conference (HRI), pp. 273–274 (2009)Google Scholar
  14. 14.
    Sorokin, A., Berenson, D., Srinivasa, S.S., Hebert, M.: People helping robots helping people: crowdsourcing for grasping novel objects. In: Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2117–2122 (2010)Google Scholar
  15. 15.
    Chernova, S., DePalma, N., Morant, E., Breazeal, C.: Crowdsourcing human-robot interaction: application from virtual to physical worlds. In: Proceeding of the 20th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 21–26 (2011)Google Scholar
  16. 16.
    Osentoski, S., Crick, C., Jay, G., Jenkins, O.C.: Crowdsourcing for closed-loop control. In: Proceeding of the Neural Information Processing Systems, NIPS 2010 Workshop on Computational Social Science and the Wisdom of Crowds, pp. 1–4 (2010)Google Scholar
  17. 17.
    Hays, J., Efros, A.A.: IM2GPS: estimating geographic information from a single image. In: Proceedings of the IEEE Conferece on Computer Vision and Pattern Recognition (CVPR) (2008)Google Scholar
  18. 18.
    Kalogerakis, E., Vesselova, O., Hays, J., Efros, A.A., Hertzmann, A.: Image sequence geolocation with human travel priors. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2009)Google Scholar
  19. 19.
    Jacobs, N., Satkin, S., Roman, N., Speyer, R., Pless, R.: Geolocating static cameras. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 1–6 (2007)Google Scholar
  20. 20.
    Simon, I., Snavely, N., Seitz, S.M.: Scene summarization for online image collections. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 1–8 (2007)Google Scholar
  21. 21.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. (SIGGRAPH Proc.) 25(3), 835–846 (2006)CrossRefGoogle Scholar
  22. 22.
    Yeh, T., Tollmar, K., Darrell, T.: Searching the web with mobile images for location recognition. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. II–76–II–81 (2004)Google Scholar
  23. 23.
    Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: Total recall: automatic query expansion with a generative feature model for object retrieval. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)Google Scholar
  24. 24.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Engineering and Computer ScienceTarleton State University, Texas A&M SystemStephenvilleUSA

Personalised recommendations