Hierarchical Multi-Modal Place Categorization

In this paper we present an hierarchical approach to place categorization. Low level sensory data is processed into more abstract concept, named properties of space. The framework allows for fusing information from heterogeneous sensory modalities and a range of derivatives of their data. Place categories are defined based on the properties that decouples them from the low level sensory data. This gives for better scalability, both in terms of memory and computations. The probabilistic inference is performed in a chain graph which supports incremental learning of the room category models. Experimental results are presented where the shape, size and appearance of the rooms are used as properties along with the number of objects of certain classes and the topology of space.

[1]  S. Lauritzen,et al.  Chain graph models and their causal interpretations , 2002 .

[2]  Ben J. A. Kröse,et al.  From images to rooms , 2007, Robotics Auton. Syst..

[3]  Ananth Ranganathan,et al.  PLISS: Detecting and Labeling Places Using Online Change-Point Detection , 2010, Robotics: Science and Systems.

[4]  Wolfram Burgard,et al.  Conceptual spatial representations for indoor mobile robots , 2008, Robotics Auton. Syst..

[5]  Achim J. Lilienthal,et al.  Incremental spectral clustering and seasons: Appearance-based localization in outdoor environments , 2008, 2008 IEEE International Conference on Robotics and Automation.

[6]  Barbara Caputo,et al.  Incremental learning for place recognition in dynamic environments , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Barbara Caputo,et al.  Visual Servoing to Help Camera Operators Track Better , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Barbara Caputo,et al.  Multi-modal Semantic Place Classification , 2010, Int. J. Robotics Res..

[9]  Ben J. A. Kröse,et al.  From sensors to human spatial concepts , 2007, Robotics Auton. Syst..

[10]  James M. Rehg,et al.  Visual Place Categorization: Problem, dataset, and algorithm , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Illah R. Nourbakhsh,et al.  Appearance-based place recognition for topological localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[12]  Guillaume Bouchard,et al.  Hierarchical part-based visual object categorization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Antonio Torralba,et al.  Context-based vision system for place and object recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  H.-A. Loeliger,et al.  An introduction to factor graphs , 2004, IEEE Signal Process. Mag..

[15]  Barbara Caputo,et al.  Confidence-based cue integration for visual place recognition , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Wolfram Burgard,et al.  Supervised Learning of Places from Range Data using AdaBoost , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[18]  Wolfram Burgard,et al.  Semantic labeling of places using information extracted from laser and vision sensor data , 2006 .

[19]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Bernt Schiele,et al.  A Semantic Typicality Measure for Natural Scene Categorization , 2004, DAGM-Symposium.

[21]  Wolfram Burgard,et al.  Supervised semantic labeling of places using information extracted from sensor data , 2007, Robotics Auton. Syst..

[22]  Frank Dellaert,et al.  Semantic Modeling of Places using Objects , 2007, Robotics: Science and Systems.

[23]  Henrik I. Christensen,et al.  The M-Space Feature Representation for SLAM , 2007, IEEE Transactions on Robotics.

[24]  James M. Rehg,et al.  Where am I: Place instance and category recognition using spatial PACT , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Joris M. Mooij,et al.  libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models , 2010, J. Mach. Learn. Res..

[26]  Barbara Caputo,et al.  The more you learn, the less you store: Memory-controlled incremental SVM for visual place recognition , 2010, Image Vis. Comput..

[27]  Cipriano Galindo,et al.  Multi-hierarchical semantic maps for mobile robotics , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Marc Hanheide,et al.  Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Robot Behaviour , 2011, IJCAI.

[29]  Roland Siegwart,et al.  Bayesian space conceptualization and place classification for semantic maps in mobile robotics , 2008, Robotics Auton. Syst..