Abstract
To operate autonomously a robot system needs among others to perceive the environment and to recognize the scene objects. In particular, nowadays an RGB-D sensor can be applied for vision-based perception. In this paper, two data-driven RGB-D image analysis steps, required for a reliable 3D object recognition process, are studied and appropriate algorithmic solutions are proposed. Clusters of 3D point features are detected in order to represent 3D object hypotheses. Particular clusters act as initial rough object hypotheses, allowing to constrain the subsequent model-based search for more distinctive object features in the image, like surface patches, textures and edges. In parallel, a 3D surface-based occupancy map is created, that delivers surface segments for the object recognition process. Test results are reported on various approaches to point feature detection and description, and point cloud processing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
ROS: Robot operating system (November 2013), http://www.ros.org
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments. Int. J. Rob. Res. 31(5), 647–663 (2012)
Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the rgb-d slam system. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 1691–1696 (May 2012)
Mörwald, T., Prankl, J., Richtsfeld, A., Zillich, M., Vincze, M.: Blort-the blocks world robotic vision toolbox. In: Best Practice in 3D Perception and Modeling for Mobile Manipulation, ICRA Workshop (2010)
Collet Romea, A., Martinez Torres, M., Srinivasa, S.: The moped framework: Object recognition and pose estimation for manipulation. International Journal of Robotics Research 30(10), 1284–1306 (2011)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Schnabel, R., Wahl, R., Klein, R.: Efficient ransac for point-cloud shape detection. Computer Graphics Forum 26(2), 214–226 (2007)
Segal, A., Haehnel, D., Thrun, S.: Generalized-icp. In: Proc. of Robotics: Science and Systems (RSS) (2009)
Kasprzak, W.: Integration of different computational models in a computer vision framework. In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 13–18 (October 2010)
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., Fitzgibbon, A.: Kinectfusion: Real-time 3d reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, UIST 2011, pp. 559–568. ACM, New York (2011)
Dryanovski, I., Valenti, R., Xiao, J.: Fast visual odometry and mapping from rgb-d data. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 2305–2310 (May 2013)
Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K.: The office marathon: Robust navigation in an indoor office environment. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 300–307 (May 2010)
Krainin, M., Henry, P., Ren, X., Fox, D.: Manipulator and object tracking for in hand 3d object modeling. Technical Report UW-CSE-10-09-01, University of Washington (2010)
Triebel, R., Pfaff, P., Burgard, W.: Multi-level surface maps for outdoor terrain mapping and loop closing. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2276–2282 (October 2006)
Wurm, K.M., Hornung, A., Bennewitz, M., Stachniss, C., Burgard, W.: Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems. In: Proc. of the ICRA 2010 Worksshop (2010)
Whelan, T., Johannsson, H., Kaess, M., Leonard, J., McDonald, J.: Robust real-time visual odometry for dense rgb-d mapping. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 5724–5731 (May 2013)
Rusu, R., Blodow, N., Marton, Z., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 3384–3391 (September 2008)
Leutenegger, S., Chli, M., Siegwart, R.: Brisk: Binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (November 2011)
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application, VISSAPP 2009, pp. 331–340. INSTICC Press (2009)
Kasprzak, W., Kornuta, T., Zieliński, C.: A virtual receptor in a robot control framework. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Recent Advances in Automation, Robotics and Measuring Techniques. AISC, vol. 267, pp. 399–408. Springer, Heidelberg (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wilkowski, A., Kornuta, T., Kasprzak, W. (2015). Point-Based Object Recognition in RGB-D Images. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-11310-4_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
eBook Packages: EngineeringEngineering (R0)