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Precise Measurement of Cargo Boxes for Gantry Robot Palletization in Large Scale Workspaces Using Low-Cost RGB-D Sensors

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10114)


This paper presents a novel algorithm for extracting the pose and dimensions of cargo boxes in a large measurement space of a robotic gantry, with sub-centimetre accuracy using multiple low cost RGB-D Kinect sensors. This information is used by a bin-packing and path-planning software to build up a pallet. The robotic gantry workspaces can be up to 10 m in all dimensions, and the cameras cannot be placed top-down since the components of the gantry actuate within this space. This presents a challenge as occlusion and sensor noise is more likely.

This paper presents the system integration components on how point cloud information is extracted from multiple cameras and fused in real-time, how primitives and contours are extracted and corrected using RGB image features, and how cargo parameters from the cluttered cloud are extracted and optimized using graph based segmentation and particle filter based techniques. This is done with sub-centimetre accuracy irrespective of occlusion or noise from cameras at such camera placements and range to cargo.


  • Point Cloud
  • Particle Filter
  • Depth Image
  • Move Little Square
  • Bilateral Filter

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  • DOI: 10.1007/978-3-319-54190-7_29
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  1. Har-peled, S.: A practical approach for computing the diameter of a point set. In: SCG 2001 (2001)

    Google Scholar 

  2. Boeing: boeing pallets @ONLINE (2012).

  3. Viegas, J.P.L., Vieira, S.M., Sousa, J.M.C., Henriques, E.M.P.: Metaheuristics for the 3D bin packing problem in the steel industry. In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, pp. 338–343 (2014)

    Google Scholar 

  4. Robotics, U.: Universal robotics @ONLINE (2015).

  5. Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition (2012)

    Google Scholar 

  6. Holz, D., Behnke, S., Holz, D., Topalidou-kyniazopoulou, A.: Real-time object detection, localization and verification for fast robotic depalletizing verification for fast robotic depalletizing. In: IROS (2015)

    Google Scholar 

  7. Lloyd, R., McCloskey, S.: Recognition of 3D package shapes for single camera metrology. In: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, pp. 99–106 (2014)

    Google Scholar 

  8. Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., Silva, C.T.: Computing and rendering point set surfaces. IEEE Trans. Vis. Comput. Graphics 9, 3–15 (2003)

    CrossRef  Google Scholar 

  9. Richtsfeld, A., Morwald, T., Prankl, J., Zillich, M., Vincze, M.: Segmentation of unknown objects in indoor environments. In: IEEE International Conference on Intelligent Robots and Systems, pp. 4791–4796 (2012)

    Google Scholar 

  10. Wu, K., Ranasinghe, R., Dissanayake, G.: A fast pipeline for textured object recognition in clutter using an RGB-D sensor. In: 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, pp. 1650–1655 (2014)

    Google Scholar 

  11. Somani, N., Cai, C., Perzylo, A., Rickert, M., Knoll, A.: Object recognition using constraints from primitive shape matching. In: 10th International Symposium on Visual Computing (ISVC 2014) (2014)

    Google Scholar 

  12. Anwer, A., Baig, A., Nawaz, R.: Calculating real world object dimensions from Kinect RGB-D image using dynamic resolution. In: Proceedings of 2015 12th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2015, pp. 198–203 (2015)

    Google Scholar 

  13. Aouada, D., Ottersten, B., Mirbach, B., Garcia, F., Solignac, T.: Real-time depth enhancement by fusion for RGB-D cameras. IET Comput. Vision 7, 335–345 (2013)

    CrossRef  Google Scholar 

  14. Wang, H., Zhang, W., Chen, Y., Chen, M., Yan, K.: Semantic decomposition and reconstruction of compound buildings with symmetric roofs from LiDAR data and aerial imagery. Remote Sens. 7, 13945–13974 (2015)

    CrossRef  Google Scholar 

  15. libfreenect2 @ONLINE (2013).

  16. iai-kinect2 @ONLINE (2015).

  17. Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)

    Google Scholar 

  18. Zhang, Z.: A flexible new technique for camera calibration (technical report). IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2002)

    CrossRef  Google Scholar 

  19. Bradski, G.: Opencv. Dr. Dobb’s journal of software tools (2000)

    Google Scholar 

  20. Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (2011)

    Google Scholar 

  21. Merry, B., Gain, J., Marais, P.: Moving least-squares reconstruction of large models with GPUs. IEEE Trans. Vis. Comput. Graphics 20, 249–261 (2014)

    CrossRef  Google Scholar 

  22. Johnson, D.B.: Finding all the elementary circuits of a directed graph. 4, 77–84 (1975)

    Google Scholar 

  23. Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: efficient position estimation for mobile robots dieter fox, wolfram burgard. In: 16th National Conference on Artificial Intelligence (AAAI99), pp. 343–349 (1999)

    Google Scholar 

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This work is funded by the Civil Aviation Authority of Singapore (CAAS) under the Aviation Challenge 2 grant.

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Correspondence to Yaadhav Raaj .

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Raaj, Y., Nair, S., Knoll, A. (2017). Precise Measurement of Cargo Boxes for Gantry Robot Palletization in Large Scale Workspaces Using Low-Cost RGB-D Sensors. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham.

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