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International Conference on Intelligent Autonomous Systems

IAS 2018: Intelligent Autonomous Systems 15 pp 86–98Cite as

An Integrated Planning of Exploration, Coverage, and Object Localization for an Efficient Indoor Semantic Mapping

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 867))

Abstract

This paper describes an integrated viewpoint planner for indoor semantic mapping. Mapping of an unknown environment can be viewed as an integration of various activities: exploration, (2D or 3D) geometrical mapping, and object detection and localization. An efficient mapping entails selecting good viewpoints. Since a good viewpoint for one activity and that for another could be shared or conflicting, it is desirable to deal with all such activities at once, in an integrated manner. We use a frontier-based exploration, an area coverage approach for geometrical mapping, and object recognition model-based verification for generative respective viewpoints, and get the best next viewpoint by solving a travelling salesman problem. We carry out experiments using a realistic 3D robotic simulator to show the effectiveness of the proposed integrated viewpoint planning method.

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Notes

  1. 1.

    The line-of-sight of the camera and the surface normal of an observed area should be within a certain angle. Currently, we use \(80^\circ \) as the threshold.

References

  1. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)

    MATH  Google Scholar 

  2. Yamauchi, B.: A frontier-based approach for autonomous navigation. In: Proceedings of the 1997 IEEE International Conference on Computational Intelligence in Robotics and Automation, pp. 146–151 (1997)

    Google Scholar 

  3. Makarenko, A.A., Williams, S.B., Bourgault, F., Durrant-Whyte, H.F.: An experiment in integrated exploration. In: Proceedings of 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 534–539 (2002)

    Google Scholar 

  4. Masuzawa, H., Miura, J.: Observation planning for environment information summarization with deadlines. In: Proceedings of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 30–36 (2010)

    Google Scholar 

  5. Ye, Y., Tsotsos, J.K.: Sensor planning for 3D object search. Comput. Vis. Image Underst. 73(2), 145–168 (1999)

    Article  Google Scholar 

  6. Sasongko, D.F., Miura, J.: An integrated exploration and observation planning for an efficient indoor 3D mapping. In: Proceedings of 2017 International Conference on Mechatronics and Automation, pp. 1924–1929 (2017)

    Google Scholar 

  7. Faigl, J., Kulich, M.: On benchmarking of frontier-based multi-robot exploration strategies. In: Proceedings of 2015 European Conference on Mobile Robots, pp. 1–8 (2015)

    Google Scholar 

  8. Basilico, N., Amigoni, F.: Exploration strategies based on multi-criteria decision making for search and rescue autonomous robots. Auton. Robots 31(4), 401–417 (2011)

    Article  Google Scholar 

  9. O’Rourke, J.: Art Gallery Theorems and Algorithms. Oxford University Press, New York (1987)

    MATH  Google Scholar 

  10. González-Baños, H., Latombe, J.-C.: A randomized art-gallery algorithm for sensor placement. In: Proceedings of 7th Annual Symposium on Computational Geometry, pp. 232–240 (2001)

    Google Scholar 

  11. Agarwal, P.K., Ezra, E., Ganjugunte, S.K.: Efficient sensor placement for surveillance problems. In: Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems, pp. 301–314 (2009)

    Chapter  Google Scholar 

  12. Ramaswamy, V., Marden, J.R.: A sensor coverage game with improved efficiency guarantees. In: Proceedings of 2016 American Control Conference, pp. 6399–6404 (2016)

    Google Scholar 

  13. Ardiyanto, I., Miura, J.: Generalized coverage solver using hybrid evolutionary optimization. Int. J. Innovative Comput. Inf. Control 13(3), 921–940 (2017)

    Google Scholar 

  14. Saidi, F., Stasse, O., Yokoi, K., Kanehiro, F.: Online object search with a humanoid robot. In: Proceedings of 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1677–1682 (2007)

    Google Scholar 

  15. Aydemir, A., Sjöö, K., Folkesson, J., Pronobis, A., Jensfelt, P.: Search in the real world: active visual object search based on spatial relations. In: Proceedings of 2011 IEEE International Conference on Robotics and Automation, pp. 2818–2824 (2011)

    Google Scholar 

  16. Rohemr, E., Singh, S.P., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1321–1326 (2013)

    Google Scholar 

  17. Elfes, A.: Sonar-based real-world mapping and navigation. Int. J. Robotics Automat. 3(3), 249–265 (1987)

    Google Scholar 

  18. Okada, Y., Miura, J.: Exploration and observation planning for 3D indoor mapping. In: Proceedings of 2015 IEEE/SICE International Symposium on System Integration, pp. 599–604 (2015)

    Google Scholar 

  19. Ardiyanto, I., Miura, J.: Visibility-based viewpoint planning for guard robot using skeletonization and geodesic motion model. In: Proceedings of the 2013 IEEE International Conference on Robotics and Automation, pp. 652–658 (2013)

    Google Scholar 

  20. Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., Neumann, F.: Local search and the traveling salesman problem: a feature-based characterization of problem hardness. In: Learning and Intelligent Optimization, vol. LNCS 7219, pp. 115–129 (2012)

    Google Scholar 

  21. Redmon, J., Farhadi, A.: YOLO9000: Better, Faster, Stronger arXiv:1612.08242 (2016)

  22. Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., Lin, T.-Y., Maire, M., Dollar, P.: Microsoft COCO: Common Objects in Context arXiv:1405.0312 (2014)

  23. Masuzawa, H., Miura, J.: Observation planning for efficient environment information summarization. In: Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5794–5800 (2009)

    Google Scholar 

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Acknowledgment

This work is in part supported by JSPS KAKENHI Grant Number 17H01799 and the Hibi Science Foundation.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan

    Diar Fahruddin Sasongko & Jun Miura

Authors
  1. Diar Fahruddin Sasongko
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  2. Jun Miura
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Corresponding author

Correspondence to Jun Miura .

Editor information

Editors and Affiliations

  1. Baden-Wuerttemberg Cooperative State University, Karlsruhe, Germany

    Marcus Strand

  2. Humanoids and Intelligence Systems Lab, KIT - Karlsruher Institut für Technologie, Karlsruhe, Germany

    Rüdiger Dillmann

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. University of Padua, Padua, Italy

    Stefano Ghidoni

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Sasongko, D.F., Miura, J. (2019). An Integrated Planning of Exploration, Coverage, and Object Localization for an Efficient Indoor Semantic Mapping. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_8

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