Combining Depth and Intensity Images to Produce Enhanced Object Detection for Use in a Robotic Colony

  • Steven BaldingEmail author
  • Darryl N. Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10454)


Robotic colonies that can communicate with each other and interact with their ambient environments can be utilized for a wide range of research and industrial applications. However amongst the problems that these colonies face is that of the isolating objects within an environment. Robotic colonies that can isolate objects within the environment can not only map that environment in detail, but interact with that ambient space. Many object recognition techniques exist, however these are often complex and computationally expensive, leading to overly complex implementations. In this paper a simple model is proposed to isolate objects, these can then be recognize and tagged. The model will be using 2D and 3D perspectives of the perceptual data to produce a probability map of the outline of an object, therefore addressing the defects that exist with 2D and 3D image techniques. Some of the defects that will be addressed are; low level illumination and objects at similar depths. These issues may not be completely solved, however, the model provided will provide results confident enough for use in a robotic colony.


Anchoring Robotic vision Sobel Depth map Robotic colony 


  1. 1.
    Milella, A., Di Paola, D., Mazzeo, P.L., Spagnolo, P., Leo, M., Cicirelli, G., D’Orazio, T.: Active surveillance of dynamic environments using a multi-agent system. IFAC Proc. 43, 13–18 (2010)CrossRefGoogle Scholar
  2. 2.
    Harnad, S.: The symbol grounding problem. Symb. Grounding Probl. D. 42, 335 (1990)Google Scholar
  3. 3.
    Coradeschi, S., Saffiotti, A.: An introduction to the anchoring problem. In: Robotics and Autonomous Systems, pp. 85–96 (2003)Google Scholar
  4. 4.
    Gwatkin, J.: Robo-CAMAL: anchoring in a cognitive robot. Doctoral dissertation, University of Hull, Kingston upon Hull, UK (2009)Google Scholar
  5. 5.
    Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: IROS, pp. 922–928 (2015)Google Scholar
  6. 6.
    Sharifzadeh, S., Centre, E., Manufacturing, I.: Edge detection techniques : evaluations and comparisons, vol. 2, pp. 1507–1520 (2008)Google Scholar
  7. 7.
    Kim, D.: Sobel operator and canny edge detector ECE 480 Fall 2013 Team 4, pp. 1–10 (2013)Google Scholar
  8. 8.
    Anusha, G.: Implementation of SOBEL edge detection on FPGA. Int. J. Comput. Trends Technol. 3, 472–475 (2012)Google Scholar
  9. 9.
    Israni, S.: Edge detection of license plate, pp. 3561–3563 (2016)Google Scholar
  10. 10.
    Lakshmi, S., Sankaranarayanan, D.V.: A study of edge detection techniques for segmentation computing approaches. Int. J. Comput. Appl. CASCT, pp. 35–41 (2010)Google Scholar
  11. 11.
    Pavithra, C., Kavitha, M., Kannan, E.: An efficient edge detection algorithm for 2D-3D conversion. In: 2014 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), pp. 434–436. IEEE (2014)Google Scholar
  12. 12.
    Adhikari, S., Kar, J., Dastidar, J.G.: An automatic and efficient foreground object extraction scheme. Int. J. Sci. Adv. Inf. Technol. 3, 40–43 (2015)Google Scholar
  13. 13.
    Abdulmajeed, R., Mansoor, R.Z.: Implementing kinect sensor for building 3D maps of indoor environments. Int. J. Comput. Appl. 86, 18–22 (2014)Google Scholar
  14. 14.
    Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the sobel operator. IEEE J. Solid-State Circ. 23, 358–367 (1988)CrossRefGoogle Scholar
  15. 15.
    Gao, W., Yang, L., Zhang, X., Liu, H.: An improved Sobel edge detection. In: Proceedings of 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010. vol. 5, pp. 67–71 (2010)Google Scholar
  16. 16.
    Bo, L., Ren, X., Fox, D.: Depth kernel descriptors for object recognition. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 821–826. IEEE (2011)Google Scholar
  17. 17.
    Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: 8th European Conference on Computer Vision, Prague, Czech Republic, Proceedings, Part III, 11–14 May 200, vol. 3023, pp. 224–237 (2004)Google Scholar
  18. 18.
    Ushma, A., Scholar, M., Shanavas, P.A.R.M.: Object detection in image processing using edge detection techniques. IOSR J. Eng. 4, 10–13 (2014)Google Scholar
  19. 19.
    Mikolajczyk, K., Zisserman, A., Schmid, C.: Shape recognition with edge-based feactures, pp. 1–10. Brit. Mach. Vis. Conf, Norwich (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Engineering and Computer ScienceUniversity of HullHullEngland

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