Robust Pallet Detection for Automated Logistics Operations

Robert Varga, Sergiu Nedevschi


A pallet detection system is presented which is designed for automated forklifts for logistics operations. The system performs stereo reconstruction and pallets are detected using a sliding window approach. In this paper we propose a candidate generation method and we introduce feature descriptors for grayscale images that are tailored to the current task. The features are designed to be invariant to certain types of illumination changes and are called normalized pair differences because of the formula involved in their calculation. Experimental results validate our approach on extensive real world data.


  1. Baglivo, L., Biasi, N., Biral, F., Bellomo, N., Bertolazzi, E., Lio, M. D., and Cecco, M. D. (2011). Autonomous pallet localization and picking for industrial forklifts: a robust range and look method. Measurement Science and Technology, 22(8):085502.
  2. Benenson, R., Omran, M., Hosang, J., and Schiele, B. (2014). Ten years of pedestrian detection, what have we learned? In ECCV-CVRSUAD. IEEE.
  3. Bostelman, R., Hong, T., and Chang, T. (2006). Visualization of pallets. In SPIE Optics East.
  4. Bourdev, L. and Brandt, J. (2005). Robust object detection via soft cascade. In CVPR, pages II: 236-243.
  5. Byun, S. and Kim, M. (2008). Real-time positioning and orienting of pallets based on monocular vision. In ICTAI (2), pages 505-508. IEEE Computer Society.
  6. Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., and Gao, W. (2010). Wld: A robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell, 32(9):1705-1720.
  7. Cucchiara, R., Piccardi, M., and Prati, A. (2000). Focus based feature extraction for pallets recognition. In BMVC.
  8. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In CVPR, pages I: 886- 893.
  9. Dollár, P., Appel, R., Belongie, S., and Perona, P. (2014). Fast feature pyramids for object detection. PAMI.
  10. Dollár, P., Belongie, S., and Perona, P. (2010). The fastest pedestrian detector in the west. In BMVC, pages 1-11. British Machine Vision Association.
  11. Dollar, P., Tu, Z. W., Perona, P., and Belongie, S. (2009). Integral channel features. In BMVC.
  12. Dollár, P., Wojek, C., Schiele, B., and Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell, 34(4):743-761.
  13. Duda, R. and Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures. CACM, 15:11-15.
  14. Hirschmuller, H. (2005). Accurate and efficient stereo processing by semi-global matching and mutual information. In CVPR, pages II: 807-814.
  15. Hough, P. V. C. (1962). A method and means for recognizing complex patterns. U.S. Patent No. 3,069,654.
  16. Katsoulas, D. and Kosmopoulos, D. I. (2001). An efficient depalletizing system based on 2d range imagery. In IEEE International Conference on Robotics and Automation, 2001. Proceedings 2001 ICRA., volume 1, pages 305-312. IEEE.
  17. Kim, W., Helmick, D., and Kelly, A. (2001). Model based object pose refinement for terrestrial and space autonomy. In International Symposium on Artificial Intelligence, Robotics, and Automation in Space, Montreal, Quebec, Canada.
  18. Mikolajczyk, K., Zisserman, A., and Schmid, C. (2003). Shape recognition with edge-based features. In BMVC.
  19. Nyga°rds, J., Högström, T., and Wernersson, A°. (2000). Docking to pallets with feedback from a sheet-of-light range camera. In IROS, pages 1853-1859. IEEE.
  20. Oh, J.-Y., Choi, H.-S., Jung, S.-H., Kim, H.-S., and Shin, H.-Y. (2013). An experimental study of pallet recognition system using kinect camera. In Advanced Science and Technology Letters Vol.42 (Mobile and Wireless 2013), pages 167-170.
  21. Ojala, T., Pietikainen, M., and Harwood, D. (1994). Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In ICPR, pages A:582-585.
  22. Ojala, T., Pietikainen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1):51-59.
  23. Pages, J., Armangue, X., Salvi, J., Freixenet, J., and Marti, J. (2011). Computer vision system for autonomous forklift vehicles in industrial environments. The 9th. Mediterranean Conference on Control and Automation.
  24. Pradalier, C., Tews, A., and Roberts, J. M. (2008). Visionbased operations of a large industrial vehicle: Autonomous hot metal carrier. J. Field Robotics, 25(4- 5):243-267.
  25. Prasse, C., Skibinski, S., Weichert, F., Stenzel, J., Müller, H., and Hompel, M. T. (2011). Concept of automated load detection for de-palletizing using depth images and RFID data. International Conference on Control System, Computing and Engineering (ICCSCE), pages 249-254.
  26. Ross, H. and Murray, D. J. (1996). E.H.Weber on the tactile senses 2nd ed. Hove: Erlbaum (UK) Taylor and Francis.
  27. Schapire, R. (1990). The strength of weak learnability. MACHLEARN: Machine Learning, 5.
  28. Seelinger, M. J. and Yoder, J.-D. (2006). Automatic visual guidance of a forklift engaging a pallet. Robotics and Autonomous Systems, 54(12):1026-1038.
  29. Spangenberg, R., Langner, T., Adfeldt, S., and Rojas, R. (2014). Large scale semi-global matching on the CPU. In Intelligent Vehicles Symposium, pages 195-201. IEEE.
  30. Varga, R. and Nedevschi, S. (2014). Vision-based automatic load handling for automated guided vehicles. In Intelligent Computer Communication and Processing, pages 239-245. IEEE.
  31. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proc. CVPR, 1:511-518.
  32. Viola, P. A., Platt, J. C., and Zhang, C. (2005). Multiple instance boosting for object detection. In NIPS.
  33. Walter, M. R., Karaman, S., Frazzoli, E., and Teller, S. J. (2010). Closed-loop pallet manipulation in unstructured environments. In IROS, pages 5119-5126. IEEE.
  34. Weichert, F., Skibinski, S., Stenzel, J., Prasse, C., Kamagaew, A., Rudak, B., and ten Hompel, M. (2013). Automated detection of euro pallet loads by interpreting PMD camera depth images. Logistics Research, 6(2- 3):99-118.
  35. Yang, M.-H., Kriegman, D. J., and Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Trans. Pattern Anal. Mach. Intell, 24(1):34-58.
  36. Zabih, R. and Woodfill, J. (1994). Non-parametric local transforms for computing visual correspondence. In ECCV, pages B:151-158.

Paper Citation

in Harvard Style

Varga R. and Nedevschi S. (2016). Robust Pallet Detection for Automated Logistics Operations . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 470-477. DOI: 10.5220/0005674704700477

in Bibtex Style

author={Robert Varga and Sergiu Nedevschi},
title={Robust Pallet Detection for Automated Logistics Operations},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},

in EndNote Style

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Robust Pallet Detection for Automated Logistics Operations
SN - 978-989-758-175-5
AU - Varga R.
AU - Nedevschi S.
PY - 2016
SP - 470
EP - 477
DO - 10.5220/0005674704700477