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Object detection and mapping for service robot tasks

Published online by Cambridge University Press:  01 March 2007

Staffan Ekvall ,
Danica Kragic  and
Patric Jensfelt
Staffan Ekvall*
Affiliation:
Computational Vision and Active Perception Laboratory, Centre for Autonomous Systems, Royal Institute of Technology, Stockholm, Sweden
Danica Kragic
Affiliation:
Computational Vision and Active Perception Laboratory, Centre for Autonomous Systems, Royal Institute of Technology, Stockholm, Sweden
Patric Jensfelt
Affiliation:
Computational Vision and Active Perception Laboratory, Centre for Autonomous Systems, Royal Institute of Technology, Stockholm, Sweden
*
*Corresponding author. E-mail: [email protected]
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Summary

The problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

Keywords

SLAMActive object recognitionReceptive field color co-occurence histograms
Type
Article
Information
Robotica , Volume 25 , Issue 2 , March 2007 , pp. 175 - 187
Copyright
Copyright © Cambridge University Press 2007

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References

1.Ballard, D. H., “Animate vision,” Artif. Intell. 48 (1), 57–86 (1991).CrossRefGoogle Scholar
2.Kuipers, B. J., “The cognitive map: Could it have been any other way?” In: Spatial Orientation: Theory, Research, and Application (Pick, H. L. Jr. and Acredolo, L. P., eds.) (Plenum, New York, 1983) pp. 345–359.CrossRefGoogle Scholar
3. CODID: CVAP Object Detection Image Database, Available at http://www.nada.kth.se/~ekvall/codid.html.Google Scholar
4.Ekvall, S., Hoffmann, F. and Kragic, D., “Object recognition and pose estimation for robotic manipulation using color cooccurrence histograms,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS'03 (2003) pp. 1284–1289.Google Scholar
5.Ekvall, S. and Kragic, D., “Receptive field cooccurrence histograms for object detection,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems, IROS'05 (2005) pp. 84–89.Google Scholar
6.Folkesson, J., Jensfelt, P. and Christensen, H. I., “Vision SLAM in the measurement subspace,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA'05 (April (2005) pp. 18–22.Google Scholar
7.Galindo, G., Saffiotti, A., Coradeschi, S., Buschka, P., Fernández-Madrigal, J. A. and González, J., “Multi-hierarchical semantic maps for mobile robotics,” Proceedings of the IEEE/RSJ International Conference Intelligent Robots and Systems, IROS'05 (2005) pp. 2278–2283.Google Scholar
8.Gonzalez, R. C. and Woods, R. E., Digital Image Processing (Addison Wesley, Reading, MA, 1992).Google Scholar
9.Gopalakrishnan, A. and Sekmen, A., “Vision-based mobile robot learning an navigation,” Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, RO-MAN'05 (2005) pp. 48–53.Google Scholar
10.Jensfelt, P., Kragic, D., Folkesson, J. and Björkman, M., “A framework for vision based bearing only 3D SLAM,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'06) (Orlando, FL, 2006) pp. 1944–1950.Google Scholar
11.Kawanishi, T., Murase, H. and Takagi, S., “Quick 3D object detection and localization by dynamic active search with multiple active cameras,” Proceedings of the IEEE International Conference on Pattern Recognition, ICPR'02 (2002) pp. 605–608.Google Scholar
12.Kleinehagenbrock, M., Fritsch, J. and Sagerer, G., “Supporting advanced interaction capabilities on a mobile robot with a flexible control system,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, vol. 4 (October (2004) pp. 3469–3655.Google Scholar
13.Kruijff, G.-J. M., Zender, H., Jensfelt, P. and Christensen, H. I., “Clarification dialogues in human-augmented mapping,” Proceedings of the 1st Annual Conference on Human–Robot Interaction, HRI'06 (Salt Lake City, UT, March (2006).CrossRefGoogle Scholar
14.Lowe, D., Perceptual Organisation and Visual Recognition. Robotics: Vision, Manipulation and Sensors (Kluwer, Dordrecht, The Netherlands), ISBN 0-89838-172-X.Google Scholar
15.MacQueen, J. B., “Some methods for classification and analysis of multivariate observations,” Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1 (University of California Press, Berkeley, 1967) pp. 281–297.Google Scholar
16.Martínez Mozos, O., Stachniss, C. and Burgard, W., “Supervised learning of places from range data using adaboost,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA'05, Barcelona, Spain (2005) pp. 1742–1747.Google Scholar
17.Michaud, F., Brosseau, Y., Cote, C., Letourneau, D., Moisan, P., Ponchon, A., Raievsky, C., Valin, J.-M., Beaudryy, E. and Kabanza, F., “Modularity and integration in the design of a socially interactive robot,” Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, RO-MAN (August (2005) pp. 172–177.Google Scholar
18.Murase, H. and Nayar, S. K., “Visual learning and recognition of 3-d objects from appearance,” Int. J. Comput. Vision 14, 5–24 (1995).CrossRefGoogle Scholar
19.Nene, S. A., Nayar, S. K. and Murase, H., “Columbia object image library: Coil-100,” Technical Report CUCS-006-96 (Department of Computer Science, Columbia University, 1996).Google Scholar
20.Newman, P. and Ho, K., “SLAM-loop closing with visually salient features,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA'05, Barcelona, Spain (2005) pp. 635–642.Google Scholar
21.Newman, P., Leonard, J., Tardós, J. D. and Neira, J., “Explore and return: Experimental validation of real-time concurrent mapping and localization,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'02), Washinton, DC (May (2002) pp. 1802–1809.Google Scholar
22.Reed, M. K., Allen, P. K. and Stamos, I., “View planning for site modeling,” Proceedings of the DARPA Image Understanding Workshop (21–23 November (1998) pp. 1181–1192.Google Scholar
23.Pacchierotti, E., Christensen, H. I. and Jensfelt, P., “Embodied social interaction in hallway settings: A user study,” Proceedings of the IEEE Workshop on Robot and Human Interactive Communication (RO-MAN'05), Nashville, TN (August (2005) pp. 164–171.Google Scholar
24.Petersson, L., Jensfelt, P., Tell, D., Strandberg, M., Kragic, D. and Christensen, H. I., “Systems integration for real-world manipulation tasks,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA'02, vol. 3 (2002) pp. 2500–2505.Google Scholar
25.Pronobis, A., Caputo, B., Jensfelt, P. and Christensen, H. I., “A discriminative approach to robust visual place recognition,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS'06 (2006).CrossRefGoogle Scholar
26.Roobaert, D., “Pedagogical support vector learning: A pure learning approach to object recognition,” Ph.D. Thesis (Computatioal Vision and Active Perception Laboratory (CVAP), Royal Institute of Technology, Stockholm, Sweden, May (2001).Google Scholar
27.Schiele, B. and Crowley, J. L., “Recognition without correspondence using multidimensional receptive field histograms,” Int. J. Comput. Vision 36 (1), 31–50 (2000).CrossRefGoogle Scholar
28.Schmid, C. and Mohr, R., “Combining grayvalue invariants with local constraints for object recognition,” Proceedings of the International Conference of Computer Vision and Pattern Recognition (2002) pp. 872–877.Google Scholar
29.Smith, R., Self, M. and Cheeseman, P., “A stochastic map for uncertain spatial relationships,” Proceedings of the 4th International Symposium on Robotics Research (1987).Google Scholar
30.Swain, M. and Ballard, D., “Color indexing,” Int. J. Compt. Vision 7, 11–32 (1991).CrossRefGoogle Scholar
31.Theobalt, C., Bos, J., Chapman, T., Espinosa, A., Fraser, M., Hayes, G., Klein, E., Oka, T. and Reeve, R., “Talking to godot: Dialogue with a mobile robot,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IROS'02 (2002) pp. 1338–1343.Google Scholar
32.Topp, E. A., Kragic, D., Jensfelt, P. and Christensen, H. I., “An interactive interface for service robots,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), New Orleans, LA (April (2004) pp. 3469–3475.Google Scholar