Spatial Correlation of Multi-sensor Features for Autonomous Victim Identification

  • Timothy Wiley
  • Matthew McGill
  • Adam Milstein
  • Rudino Salleh
  • Claude Sammut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)


Robots are used for Urban Search and Rescue to assist rescue workers. To enable the robots to find victims, they are equipped with various sensors including thermal, video and depth time-of-flight cameras, and laser range-finders. We present a method to enable a robot to perform this task autonomously. Thermal features are detected using a dynamic temperature threshold. By aligning the thermal and time-of-flight camera images, the thermal features are projected into 3D space. Edge detection on laser data is used to locate holes within the environment, which are then spatially correlated to the thermal features. A decision tree uses the correlated features to direct the autonomous policy to explore the environment and locate victims. The method was evaluated in the 2010 RoboCup Rescue Real Robots Competition.


Spatial Correlation Extend Kalman Filter Thermal Image Feature Detection Hole Centre 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Birk, A., Kenn, H., Carpin, S., Pfingsthorn, M.: Toward Autonomous Rescue Robots. In: Proceedings of the First International Workshop on Synthetic Simulation and Robotics to Mitigate Earthquake Disasters (2003)Google Scholar
  2. 2.
    Gumhold, S., Wang, X., MacLeod, R.: Feature extraction from point clouds. In: Proceedings of the 10th International Meshing Roundtable, vol. 2001, pp. 293–305 (2001)Google Scholar
  3. 3.
    Johnson, S.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)CrossRefGoogle Scholar
  4. 4.
    Markov, S., Birk, A.: Detecting Humans in 2D Thermal Images by Generating 3D Models. In: Hertzberg, J., Beetz, M., Englert, R. (eds.) KI 2007. LNCS (LNAI), vol. 4667, pp. 293–307. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Meyer, J., Schnitzspan, P., Kohlbrecher, S., Petersen, K., Andriluka, M., Schwahn, O., Klingauf, U., Roth, S., Schiele, B., von Stryk, O.: A Semantic world Model for Urban Search and Rescue Based on Heterogeneous Sensors. In: Ruiz-del-Solar, J. (ed.) RoboCup 2010. LNCS (LNAI), vol. 6556, pp. 180–193. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Milstein, A., McGill, M., Wiley, T., Salleh, R., Sammut, C.: Occupancy Vozel Metric Based Iterative Closest Point for Position Tracking in 3D Environments. In: 2011 IEEE International Conference on Robotics and Automation, ICRA (May 2011)Google Scholar
  7. 7.
    Nourbakhsh, I.R., Sycara, K., Koes, M., Yong, M., Lewis, M., Burion, S.: Human-robot teaming for Search and Rescue. IEEE Pervasive Computing 4(1), 72–77 (2005)CrossRefGoogle Scholar
  8. 8.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems Science and Cybernetics SMC-9(1), 62–66 (1979)Google Scholar
  9. 9.
    Pellenz, J., Gossow, D., Paulus, D.: Robbie: A Fully Autonomous Robot for RoboCupRescue. Advanced Robotics 23(9), 1159–1177 (2009)CrossRefGoogle Scholar
  10. 10.
    Shapiro, L., Stockman, G.: Computer vision, ch. 3. Prentice Hall (2001)Google Scholar
  11. 11.
    Weber, C., Hamann, S., Hagen, H.: Sharp Feature Detection in Point Clouds. In: IEEE International Conference on Shape Modeling and Applications, SMI (2010)Google Scholar
  12. 12.
    Wiley, T.: Autonomous Victim Identification, Honours Thesis, The University of New South Wales (August 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Timothy Wiley
    • 1
  • Matthew McGill
    • 1
  • Adam Milstein
    • 1
  • Rudino Salleh
    • 1
  • Claude Sammut
    • 1
  1. 1.The School of Computer Science and EngineeringThe University of New South Wales, UNSWSydneyAustralia

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