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Object Modeling and Recognition from Sparse, Noisy Data via Voxel Depth Carving

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Experimental Robotics

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 109))

Abstract

In this work, we make the case for using volumetric information for shape reconstruction and recognition from noisy depth images for robotic manipulation. We provide an efficient algorithm, Voxel Depth Carving (a variant of Occupancy Grid Mapping) which accomplishes this goal. Real-world experiments with lasers, RGB-D cameras, and simulated sensors in both 2D and 3D verify the effectiveness of our algorithm in comparison to traditional point-cloud based methods.

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References

  1. Besl, P.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. (1992)

    Google Scholar 

  2. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vision 70(2), 109–131 (2006)

    Article  Google Scholar 

  3. Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)

    Google Scholar 

  4. Canterakis, N.: 3D Zernike moments and Zernike affine invariants for 3D image analysis and recognition. In: 11th ICSA (1999)

    Google Scholar 

  5. Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer (1989) (Long Beach, CA)

    Google Scholar 

  6. Ford, L.R., Fulkerson, D.R., A simple algorithm for finding maximal network flows and an application to the hitchcock problem. Can. J. Math. (1957)

    Google Scholar 

  7. Goldfeder, C.: Data-driven grasping. Auton. Robots 31(1), 1–20 (2011)

    Article  Google Scholar 

  8. Herrmann, M.: Exploiting passthrough information for multi-view object reconstruction with sparse and noisy laser data (2010)

    Google Scholar 

  9. Hinterstoisser, S., Lepetit, V., Ilic, S.: Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. ACCV 7724, 548–562 (2013)

    Google Scholar 

  10. Hoiem, D., Savarese, S.: Representations and techniques for 3D object recognition and scene interpretation. Synth. Lect. AI Mach. Learn. 5(5), 1–169 (2011)

    Google Scholar 

  11. Hornung, A., Kobbelt, L.: Robust reconstruction of watertight 3D models from non-uniformly sampled point clouds without normal information. SGP (2006)

    Google Scholar 

  12. Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34(3), 189–206 (2013)

    Article  Google Scholar 

  13. Hsiao, K., Ciocarlie, M., Brook, P.: Bayesian grasp planning. ICRA 2011, (2011)

    Google Scholar 

  14. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 66–70 (1962)

    Google Scholar 

  15. Kutulakos, K.N., Seitz, S.M.: A theory of shape by space carving. ICCV 1, (1999)

    Google Scholar 

  16. Lai, K., Fox, D.: Object recognition in 3D point clouds using web data and domain adaptation. IJRR 29(8), 1019–1037 (2010)

    Google Scholar 

  17. Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Trans. Pattern Anal. Mach. Intell. 16(2) (1994)

    Google Scholar 

  18. Li, Y., Wu, X., Chrysathou, Y., Sharf, A.: GlobFit: consistently fitting primitives by discovering global relations. ACM Trans. Graph. (2011)

    Google Scholar 

  19. Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph 21(4), 163–169 (1987)

    Article  Google Scholar 

  20. Marton, Z.C., Rusu, R.B., Beetz, M.: On fast surface reconstruction methods for large and noisy point clouds. ICRA 3218–3223 (2009)

    Google Scholar 

  21. Nguyen, C.V., Izadi, S., Lovell, D.: Modeling kinect sensor noise for improved 3D reconstruction and tracking. In: 3D Imaging, Modeling, pp. 524–530 (2012)

    Google Scholar 

  22. Olsen, M.J.: Avoiding indicents with incidence. LIDAR Mag. 2, 2 (2012)

    Google Scholar 

  23. Pajarola, R., Guggeri, F., Scateni, R.: Shape reconstruction from raw point clouds using depth carving. Eurographics (2012)

    Google Scholar 

  24. Prasad, D.K.: Survey of the problem of object detection in real images. IJIP 6, 441–466 (2012)

    Google Scholar 

  25. Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). ICRA 1–4 (2011)

    Google Scholar 

  26. Shewchuk, J.R., Brien, J.F.O.: Spectral surface reconstruction from noisy point clouds. SGP 14 (2004)

    Google Scholar 

  27. Shilane, P., Min, P.: The princeton shape benchmark. Shape Model. 08540 (2004)

    Google Scholar 

  28. Staples-moore, A.: Network flows and the max-flow min-cut theorem

    Google Scholar 

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Correspondence to Matthew Klingensmith .

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Klingensmith, M., Herrmann, M., Srinivasa, S.S. (2016). Object Modeling and Recognition from Sparse, Noisy Data via Voxel Depth Carving. In: Hsieh, M., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-319-23778-7_46

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  • DOI: https://doi.org/10.1007/978-3-319-23778-7_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23777-0

  • Online ISBN: 978-3-319-23778-7

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