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Intelligent Autonomous Systems 13 pp 913–922Cite as

Visual Mapping in Light-Crowded Indoor Environments

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

Due to the recent success of affordable RGBD cameras, solutions to the Visual Simultaneous Localization and Mapping (VSLAM) problem has experienced a huge leap. To enable accurate mapping solutions, most of the proposed solutions expect static environments. Thinking of industrial applications, there is no guarantee for static environments. The SLAM algorithm has to cope with moving objects like human beings. We present an approach to detect moving objects in RGBD camera images. The approach is based on point cloud and image filtering techniques. We present test results using publicly available datasets. We further show the performance and influence of the algorithm on mapping and on the accuracy of a visual SLAM system.

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Notes

  1. 1.

    https://www.iti.uni-luebeck.de/navigation.

  2. 2.

    http://www.mobilerobots.com/ResearchRobots/PeopleBot.aspx.

References

  1. Lowe, D.: Object recognition from local scale-invariant features. In: 7th IEEE International Conference on Computer Vision (ICCV). (1999).

    Google Scholar 

  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding (2008).

    Google Scholar 

  3. Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) (2010).

    Google Scholar 

  4. Hartmann, J., Kluessendorff, J.H., Maehle, E.: A Unified Visual Graph-Based Approach to Navigation for Wheeled Mobile Robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (2013).

    Google Scholar 

  5. Kluessendorff, J.H., Hartmann, J., Forouher, D., Maehle, E.: Graph-Based Visual SLAM and Visual Odometry using an RGB-D Camera. In: 9th International Workshop on Robot Motion and Control (RoMoCo). (2013).

    Google Scholar 

  6. Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An Evaluation of the RGB-D SLAM System. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA). (2012).

    Google Scholar 

  7. 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. The International Journal of Robotics Research (2012).

    Google Scholar 

  8. Grisetti, G., Stachniss, C., Burgard, W.: Nonlinear Constraint Network Optimization for Efficient Map Learning. IEEE Transactions on Intelligent Transportation Systems (2009).

    Google Scholar 

  9. Stückler, J., Behnke, S.: Multi-Resolution Surfel Maps for Efficient Dense 3D Modeling and Tracking. In: Journal of Visual Communication and Image Representation. (2013).

    Google Scholar 

  10. Choi, W., Pantofaru, C., Savarese, S.: Detecting and tracking people using an RGB-D camera via multiple detector fusion. In: IEEE International Conference on Computer Vision Workshops ICCV Workshops. (2011).

    Google Scholar 

  11. Munaro, M., Basso, F., Menegatti, E.: Tracking people within groups with RGB-D data. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012).

    Google Scholar 

  12. Zhang, H., Reardon, C., Parker, L.E.: Real-time multiple human perception with color-depth cameras on a mobile robot. IEEE Transactions on Cybernetics (2013).

    Google Scholar 

  13. Kuemmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: A general framework for graph optimization. In: IEEE International Conference on Robotics and Automation (ICRA). (2011).

    Google Scholar 

  14. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Computer Vision ECCV 2010. Lecture Notes in Computer Science. Springer, Berlin / Heidelberg (2010).

    Google Scholar 

  15. Hartmann, J., Kluessendorff, J.H., Maehle, E.: A Comparison of Feature Descriptors for Visual SLAM. In: 6th European Conference on Mobile Robots (ECMR). (2013).

    Google Scholar 

  16. Holz, D., Trevor, A.J.B., Dixon, M., Gedikli, S., Rusu, B.: Fast segmentation of RGB-D images for semantic scene understanding. In: ICRA Workshop on Semantic Perception and Mapping for Knowledge-enabled Service Robotics. (2012).

    Google Scholar 

  17. Linán, C.C.: cvBlob. http://cvblob.googlecode.com

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Author information

Authors and Affiliations

  1. Institute of Computer Engineering, University of Luebeck, Ratzeburger Allee 160, 23562, Luebeck, Germany

    Jan Helge Klüssendorff, Kristian Ehlers & Erik Maehle

Authors
  1. Jan Helge Klüssendorff
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  2. Kristian Ehlers
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  3. Erik Maehle
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Corresponding author

Correspondence to Jan Helge Klüssendorff .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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© 2016 Springer International Publishing Switzerland

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Klüssendorff, J.H., Ehlers, K., Maehle, E. (2016). Visual Mapping in Light-Crowded Indoor Environments. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_66

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

  • eBook Packages: EngineeringEngineering (R0)

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