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Active Scene Analysis Based on Multi-Sensor Fusion and Mixed Reality on Mobile Systems

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Foundations and Practical Applications of Cognitive Systems and Information Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 215))

Abstract

The approach presented shows possible ways of improving scene analysis to achieve more reliable and accurate object recognition in the context of mobile robotics. The centralized architecture combines different feature detectors with active modalities, such as change of perspective or influencing the scene. It opens possibilities for the use of 2D detectors and extends the results to 3D. In combination with mixed reality, it offers the possibility of evaluation of the developed system as well as increased efficiency. The architecture developed and the preliminary results are presented. The work goes a step in the direction of active intelligent perception.

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Notes

  1. 1.

    http://www.roboearth.org/

  2. 2.

    http://ecto.willowgarage.com/recognition/user.html

  3. 3.

    http://www.ros.org

  4. 4.

    http://www.willowgarage.com/pages/pr2/overview

  5. 5.

    http://opencv.willowgarage.com/wiki/

  6. 6.

    http://pointclouds.org/

  7. 7.

    http://www.postgresql.org/

References

  1. Rusu RB, Bradski G, Thibaux R, Hsu J (2010) Fast 3D recognition and pose using the viewpoint feature histogram. Proceedings of IROS

    Google Scholar 

  2. Sun M, Xu B, Bradski G, Savarese S (2010) Depth-encoded hough voting for joint object detection and shape recovery. Proceedings of ECCV

    Google Scholar 

  3. Rusu RB, Blodow N, Marton ZC, Beetz M (2009) Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments. Proceedings of IROS

    Google Scholar 

  4. Michalicek G, Klimentjew D, Zhang J (2011) A 3D simultaneous localization and mapping exploration system. In: Proceedings of the IEEE international conference on robotics and biomimetics (IEEE-ROBIO), Phuket, Thailand, pp 1059–1065, ISBN: 978-4577-2137-3, 7-11 Dec 2011

    Google Scholar 

  5. Klimentjew D, Zhang J (2011) Adaptive sensor-fusion of depth and color information for cognitive robotics. In: Proceedings of the IEEE international conference on robotics and biomimetics (IEEE-ROBIO), Phuket, Thailand, pp 957–962, ISBN: 978-4577-2137-3, 7-11 Dec 2011

    Google Scholar 

  6. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  7. Bay H, Tuytelaars T, Van Gool L (2006) Speeded up robust features, Proceedings of the 9th European conference on computer vision. Springer, New York

    Google Scholar 

  8. Roth PM, Winter M (2008) Survey of appearance-based methods for object recognition, Technical report ICG-TR-01/08. Graz University of Technology, Austria

    Google Scholar 

  9. Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm, Third international conference on 3D digital imaging and modeling (3DIM 2001). Quebec City, Canada

    Google Scholar 

  10. Rockel S, Klimentjew D, Zhang J (2012) A multi-robot platform for mobile robots-a novel evaluation and development approach with multi-agent technology. Proceedings of the IEEE international conference on multisensor fusion and integration for intelligent systems (MFI), University of Hamburg, Hamburg, Germany, In

    Google Scholar 

  11. Low K-L, Lastra A (2006) Efficient constraint evaluation algorithms for hierarchical next-best-view planning 3D data processing, visualization, and transmission, third international symposium on, vol., no., pp. 830–837, 14–16 June 2006, doi:10.1109/3DPVT.2006.52

    Google Scholar 

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Acknowledgments

The authors would like to thank William Morris for his structural feedback and Robert Wieczoreck for supporting the implementation of the calibration procedure. This work has been conducted as part of RACE, funded under the European Community’s Seventh Framework Programme FP7-ICT-2011-7 under grant agreement n 287752 (http://www.project-race.eu/).

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Correspondence to Denis Klimentjew .

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Klimentjew, D., Rockel, S., Zhang, J. (2014). Active Scene Analysis Based on Multi-Sensor Fusion and Mixed Reality on Mobile Systems. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_69

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  • DOI: https://doi.org/10.1007/978-3-642-37835-5_69

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37834-8

  • Online ISBN: 978-3-642-37835-5

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