Abstract
In urban search and rescue scenarios, typical applications of robots include autonomous exploration of possibly dangerous sites, and the recognition of victims and other objects of interest. In complex scenarios, relying on only one type of sensor is often misleading, while using complementary sensors frequently helps improving the performance. To that end, we propose a probabilistic world model that leverages information from heterogeneous sensors and integrates semantic attributes. This method of reasoning about complementary information is shown to be advantageous, yielding increased reliability compared to considering all sensors separately. We report results from several experiments with a wheeled USAR robot in a complex indoor scenario. The robot is able to learn an accurate map, and to detect real persons and signs of hazardous materials based on inertial sensing, odometry, a laser range finder, visual detection, and thermal imaging. The results show that combining heterogeneous sensor information increases the detection performance, and that semantic attributes can be successfully integrated into the world model.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Asada, M., Shirai, Y.: Building a world model for a mobile robot using dynamic semantic constraints. In: IJCAI 1989, pp. 1629–1634 (1989)
Burgard, W., Hebert, M.: World modeling. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 853–869. Springer, Heidelberg (2008)
Kumar, S., Guivant, J., Durrant-Whyte, H.: Informative representations of unstructured environments. In: ICRA (2004)
Tadokoro, S., et al.: Robocup rescue project. Advanced Robotics 14(5), 423–425 (2000)
Kleiner, A., Kümmerle, R.: Genetic MRF model optimization for real-time victim detection in search and rescue. In: IROS (2007)
Rottmann, A., Mozos, O.M., Stachniss, C., Burgard, W.: Semantic place classification of indoor environments with mobile robots using boosting. In: AAAI (2005)
Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: People detection and articulated pose estimation. In: CVPR (2009)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A Discriminatively Trained, Multiscale, Deformable Part Model. In: CVPR (2008)
Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3d human pose annotations. In: ICCV (2009)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR (2005)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)
Ferrari, V., Marin, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: CVPR (2009)
Schnitzspan, P., Fritz, M., Schiele, B.: Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 527–540. Springer, Heidelberg (2008)
Wojek, C., Dorkó, G., Schulz, A., Schiele, B.: Sliding-Windows for Rapid Object Class Localization: A Parallel Technique. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 71–81. Springer, Heidelberg (2008)
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: CVPR (2009)
Jüngling, K., Arens, M.: Feature based person detection beyond the visible spectrum. In: CVPR Recognition Workshops (2009)
Davis, J., Sharma, V.: Robust detection of people in thermal imagery. In: ICPR 2004 (2004)
Pham, Q.C., Gond, L., Begard, J., Allezard, N., Sayd, P.: Real-time posture analysis in a crowd using thermal imaging. In: CVPR (2007)
Markov, S., Birk, A.: Detecting humans in 2d thermal images by generating 3d models. In: Hertzberg, J., Beetz, M., Englert, R. (eds.) KI 2007. LNCS (LNAI), vol. 4667, pp. 293–307. Springer, Heidelberg (2007)
Fod, A., Howard, A., Mataric, M.J.: Laser-based people tracking. In: ICRA (2002)
Arras, K., Grzonka, S., Luber, M., Burgard, W.: Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities. In: ICRA (2008)
Carballo, A., Ohya, A., Yuta, S.: Multiple people detection from a mobile robot using double layered laser range finders. In: ICRA Workshop (2009)
Zivkovic, Z., Kröse, B.: Part based people detection using 2d range data and images. In: ICRA (2007)
Gate, G., Breheret, A., Nashashibi, F.: Centralized fusion for fast people detection in dense environment. In: ICRA (2009)
Schiele, B., Crowley, J.: A comparison of position estimation techniques using occupancy grids. In: ICRA (1994)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meyer, J. et al. (2011). A Semantic World Model for Urban Search and Rescue Based on Heterogeneous Sensors. In: Ruiz-del-Solar, J., Chown, E., Plöger, P.G. (eds) RoboCup 2010: Robot Soccer World Cup XIV. RoboCup 2010. Lecture Notes in Computer Science(), vol 6556. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20217-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-20217-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20216-2
Online ISBN: 978-3-642-20217-9
eBook Packages: Computer ScienceComputer Science (R0)