Topological spatial relations for active visual search

If robots are to assume their long anticipated place by humanity's side and be of help to us in our partially structured environments, we believe that adopting human-like cognitive patterns will be valuable. Such environments are the products of human preferences, activity and thought; they are imbued with semantic meaning. In this paper we investigate qualitative spatial relations with the aim of both perceiving those semantics, and of using semantics to perceive. More specifically, in this paper we introduce general perceptual measures for two common topological spatial relations, ''on'' and ''in'', that allow a robot to evaluate object configurations, possible or actual, in terms of those relations. We also show how these spatial relations can be used as a way of guiding visual object search. We do this by providing a principled approach for indirect search in which the robot can make use of known or assumed spatial relations between objects, significantly increasing the efficiency of search by first looking for an intermediate object that is easier to find. We explain our design, implementation and experimental setup and provide extensive experimental results to back up our thesis.

[1]  Lambert E. Wixson,et al.  Using intermediate objects to improve the efficiency of visual search , 1994, International Journal of Computer Vision.

[2]  Loredana Cornero,et al.  Women , 1893, The Hospital.

[3]  Danica Kragic,et al.  Object detection and mapping for service robot tasks , 2007, Robotica.

[4]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

[5]  Leonard Talmy,et al.  Force Dynamics in Language and Cognition , 1987, Cogn. Sci..

[6]  Tamim Asfour,et al.  Active multi-view object search on a humanoid head , 2009, 2009 IEEE International Conference on Robotics and Automation.

[7]  S. Levinson,et al.  'Natural Concepts' in the Spatial Topologial Domain--Adpositional Meanings in Crosslinguistic Perspective: An Exercise in Semantic Typology , 2003 .

[8]  R. Siegwart,et al.  Cognitive Spatial Representations for Mobile Robots-Perspectives from a user study , 2006 .

[9]  Danica Kragic,et al.  Object Search and Localization for an Indoor Mobile Robot , 2009, J. Comput. Inf. Technol..

[10]  G. Lakoff.  Women, fire, and dangerous things : what categories reveal about the mind , 1989 .

[11]  Wolfram Burgard,et al.  Probabilistic Rule Set Joint State Update as approximation to the full joint state estimation applied to multi object scene analysis , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Dejan Pangercic,et al.  Combining perception and knowledge processing for everyday manipulation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  S. Ullman,et al.  Spatial Context in Recognition , 1996, Perception.

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

[15]  John K. Tsotsos,et al.  Attention and Visual Search: Active Robotic Vision Systems that Search , 2007 .

[16]  John K. Tsotsos.  On the relative complexity of active vs. passive visual search , 2004, International Journal of Computer Vision.

[17]  John K. Tsotsos,et al.  A theory of active object localization , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[18]  Jizhong Xiao,et al.  3 D Indoor Mapping for Micro-UAVs Using Hybrid Range Finders and Multi-Volume Occupancy Grids , 2010 .

[19]  Laura A. Carlson,et al.  Grounding spatial language in perception: an empirical and computational investigation. , 2001, Journal of experimental psychology. General.

[20]  Roland Siegwart,et al.  Cognitive Spatial Representations for Mobile Robots , 2007 .

[21]  Chandana Paul,et al.  Hybrid laser and vision based object search and localization , 2008, 2008 IEEE International Conference on Robotics and Automation.

[22]  Jun Miura,et al.  Observation planning for environment information summarization with deadlines , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Yiming Ye,et al.  Sensor Planning for 3D Object Search , 1999 .

[24]  John K. Tsotsos,et al.  Visual search for an object in a 3D environment using a mobile robot , 2010, Comput. Vis. Image Underst..

[25]  Joel W. Burdick,et al.  A probabilistic framework for object search with 6-DOF pose estimation , 2011, Int. J. Robotics Res..

[26]  Anthony G. Cohn,et al.  Qualitative Spatial Representation and Reasoning: An Overview , 2001, Fundam. Informaticae.

[27]  Yiming Ye,et al.  Where to look next in 3D object search , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[28]  B. S. Manjunath,et al.  Subset selection for active object recognition , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[29]  F. J. Langdon,et al.  The Child's Conception of Space , 1967 .

[30]  Klaus-Peter Gapp.  Basic Meanings of Spatial Relations: Computation and Evaluation in 3D Space , 1994, AAAI.

[31]  Alexei A. Efros,et al.  Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics , 2010, ECCV.

[32]  Olivier Stasse,et al.  Active Visual Search by a Humanoid Robot , 2007 .

[33]  Patric Jensfelt,et al.  Object search on a mobile robot using relational spatial information , 2010 .

[34]  James J. Little,et al.  Curious George: An attentive semantic robot , 2008, Robotics Auton. Syst..

[35]  Hiroshi Murase,et al.  Quick 3D object detection and localization by dynamic active search with multiple active cameras , 2002, Object recognition supported by user interaction for service robots.

[36]  Annette Herskovits,et al.  Language and spatial cognition , 1986 .

[37]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[38]  M. Bar.  Visual objects in context , 2004, Nature Reviews Neuroscience.

[39]  Richtsfeld Andreas,et al.  Taking in Shape: Detection and Tracking of Basic 3D Shapes in a Robotics Context , 2010 .

[40]  John D. Kelleher.  A perceptually based computational framework for the interpretation of spatial language , 2003 .

[41]  John K. TsotsosDepartment.  Sensor Planning for Object Search Sensor Planning for 3d Object Search , 1996 .

[42]  Kenneth D. Forbus,et al.  Automatic Categorization of Spatial Prepositions , 2006 .

[43]  Jan-Olof Eklundh,et al.  Vision in the real world: Finding, attending and recognizing objects , 2006, Int. J. Imaging Syst. Technol..

[44]  F. J. Langdon,et al.  The Child's Conception of Space , 1967 .

[45]  Kenny R. Coventry,et al.  Seeing, saying and acting: The psychological semantics of spatial prepositions , 2004 .

[46]  S. Levinson,et al.  LANGUAGE AND SPACE , 1996 .

[47]  Heiko Wersing,et al.  Active 3D Object Localization Using a Humanoid Robot , 2011, IEEE Transactions on Robotics.

[48]  Charles C. Kemp,et al.  RF vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[49]  T. Garvey.  Perceptual strategies for purposive vision , 1975 .

[50]  Wolfram Burgard,et al.  OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems , 2010 .

[51]  G. Lakoff,et al.  Women, Fire, and Dangerous Things: What Categories Reveal about the Mind , 1988 .

[52]  Geoffrey A. Hollinger,et al.  Combining search and action for mobile robots , 2009, 2009 IEEE International Conference on Robotics and Automation.

[53]  Patric Jensfelt,et al.  Mechanical support as a spatial abstraction for mobile robots , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.