Model-Instance Object Mapping

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8992)


Robot localization and mapping algorithms commonly represent the world as a static map. In reality, human environments consist of many movable objects like doors, chairs and tables. Recognizing that such environment often have a large number of instances of a small number of types of objects, we propose an alternative approach, Model-Instance Object Mapping that reasons about the models of objects distinctly from their different instances. Observations classified as short-term features by Episodic non-Markov Localization are clustered to detect object instances. For each object instance, an occupancy grid is constructed, and compared to every other object instance to build a directed similarity graph. Common object models are discovered as strongly connected components of the graph, and their models as well as distribution of instances saved as the final Model-Instance Object Map. By keeping track of the poses of observed instances of object models, Model-Instance Object Maps learn the most probable locations for commonly observed object models. We present results of Model-Instance Object Mapping over the course of a month in our indoor office environment, and highlight the common object models thus learnt in an unsupervised manner.


  1. 1.
    Biswas, J., Veloso, M.: Episodic Non-Markov localization: reasoning about short-term and long-term features. In: ICRA (2014)Google Scholar
  2. 2.
    Walcott-Bryant, A., Kaess, M., Johannsson, H., Leonard, J.: Dynamic pose graph slam: long-term mapping in low dynamic environments. In: IROS, pp. 1871–1878 (2012)Google Scholar
  3. 3.
    Biber, P., Duckett, T.: Dynamic maps for long-term operation of mobile service robots. In: RSS 2005, pp. 17–24 (2005)Google Scholar
  4. 4.
    Meyer-Delius, D., Hess, J., Grisetti, G., Burgard, W.: Temporary maps for robust localization in semi-static environments. In: IROS 2012, pp. 5750–5755 (2012)Google Scholar
  5. 5.
    Saarinen, J., Andreasson, H., Lilienthal, A.: Independent markov chain occupancy grid maps for representation of dynamic environment. In: IROS 2012, pp. 3489–3495 (2012)Google Scholar
  6. 6.
    Stachniss, C., Burgard, W.: Mobile robot mapping and localization in non-static environments. In: AAAI 2005, pp. 1324–1329 (2005)Google Scholar
  7. 7.
    Anguelov, D., Biswas, R., Koller, D., Limketkai, B., Thrun, S.: Learning hierarchical object maps of non-stationary environments with mobile robots. In: UAI 2002, pp. 10–17 (2002)Google Scholar
  8. 8.
    Biswas, R., Limketkai, B., Sanner, S., Thrun, S.: Towards object mapping in non-stationary environments with mobile robots. In: 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 1014–1019. IEEE (2002)Google Scholar
  9. 9.
    Wang, C.-C., Thorpe, C., Thrun, S., Hebert, M., Durrant-Whyte, H.: Simultaneous localization, mapping and moving object tracking. The Int. J. Rob. Res. 26(9), 889–916 (2007)CrossRefGoogle Scholar
  10. 10.
    Limketkai, B., Liao, L., Fox, D.: Relational object maps for mobile robots. In: IJCAI, pp. 1471–1476 (2005)Google Scholar
  11. 11.
    Modayil, J., Kuipers, B.: Bootstrap learning for object discovery. In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), vol. 1, pp. 742–747. IEEE (2004)Google Scholar
  12. 12.
    Gallagher, G., Srinivasa, S.S., Bagnell, J.A., Ferguson, D.: Gatmo: a generalized approach to tracking movable objects. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 2043–2048. IEEE (2009)Google Scholar
  13. 13.
    Rusu, R.B.: Semantic 3d object maps for everyday manipulation in human living environments. KI-Künstliche Intelligenz 24(4), 345–348 (2010)CrossRefGoogle Scholar
  14. 14.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  15. 15.
    Biswas, J., Veloso, M.M.: Localization and navigation of the cobots over long-term deployments. The Int. J. Rob. Res. 32(14), 1679–1694 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations