Qualitative distances and qualitative image descriptions for representing indoor scenes in robotics

Patterns of qualitative concepts are extracted from robot sensors in order to describe the shapes, colours, spatial orientations and topology situations of natural landmarks in the robot environment and also the distance to them. Those qualitative patterns are obtained at a low level sensor data processing and without using training on datasets or learning techniques. A qualitative distance integration approach is parametrized and applied to detect glass windows and mirrors. Corners and columns are detected by the laser sensor and described qualitatively as relevant landmarks. Images taken by the robot camera are described qualitatively for completing the description of the objects located in the robot environment. Experimentation carried out shows that the integration of the information provided enhances the robot perception.

[1]  Ben J. A. Kröse,et al.  From Sensors to Human Spatial Concepts: An Annotated Data Set , 2008, IEEE Transactions on Robotics.

[2]  Frank Dellaert,et al.  Bayesian surprise and landmark detection , 2009, 2009 IEEE International Conference on Robotics and Automation.

[3]  Shyamanta M. Hazarika,et al.  Qualitative Spatio-Temporal Representation and Reasoning: Trends and Future Directions , 2012 .

[4]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[5]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[6]  Honghai Liu,et al.  A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Quantitative Representations , 2008, IEEE Transactions on Fuzzy Systems.

[7]  Luis González Abril,et al.  A model for the qualitative description of images based on visual and spatial features , 2012, Comput. Vis. Image Underst..

[8]  Jianwei Zhang,et al.  Multi sensor fusion of camera and 3D laser range finder for object recognition , 2010, 2010 IEEE Conference on Multisensor Fusion and Integration.

[9]  Christoph Schlieder,et al.  Reasoning About Ordering , 1995, COSIT.

[10]  Zoe Falomir,et al.  A Model for Qualitative Colour Description and Comparison , 2011 .

[11]  David W. Krout,et al.  Video data and sonar data: Real world data fusion example , 2011, 14th International Conference on Information Fusion.

[12]  Steven Reece,et al.  Qualitative model-based multisensor data fusion and parameter estimation using ∞-norm Dempster-Shafer evidential reasoning , 1997, Defense, Security, and Sensing.

[13]  Diego González-Aguilera,et al.  Camera and Laser Robust Integration in Engineering and Architecture Applications , 2010 .

[14]  Frank Dylla,et al.  SailAway: Formalizing Navigation Rules , 2007 .

[15]  Xinde Li,et al.  Robot Map Building from Sonar and Laser Information using DSmT with Discounting Theory , 2007 .

[16]  Katsushi Ikeuchi,et al.  Fusion of a camera and a laser range sensor for vehicle recognition , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[17]  Andrew U. Frank,et al.  Theories and Methods of Spatio-Temporal Reasoning in Geographic Space , 1992, Lecture Notes in Computer Science.

[18]  Mikel M. Miller,et al.  Navigation in Difficult Environments: Multi-Sensor Fusion Techniques , 2012 .

[19]  Bernhard Nebel,et al.  Qualitative Spatial Reasoning for Rule Compliant Agent Navigation , 2007, FLAIRS Conference.

[20]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[21]  Diedrich Wolter,et al.  Qualitative Spatial Reasoning for Applications: New Challenges and the SparQ Toolbox , 2012 .

[22]  Shuzhi Sam Ge,et al.  Online map building for autonomous mobile robots by fusing laser and sonar data , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[23]  Zoe Falomir,et al.  Qualitative distances and qualitative description of images for indoor scene description and recognition in robotics , 2012, AI Commun..

[24]  Zoe Falomir,et al.  Fuzzy Distance Sensor Data Integration and Interpretation , 2011, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[25]  H. Barlow Vision Science: Photons to Phenomenology by Stephen E. Palmer , 2000, Trends in Cognitive Sciences.

[26]  Steven Reece,et al.  Self-Adaptive Multi-sensor Systems , 2000, IWSAS.

[27]  Gaurav S. Sukhatme,et al.  People tracking and following with mobile robot using an omnidirectional camera and a laser , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[28]  Hugh F. Durrant-Whyte,et al.  A Qualitative Approach to Sensor Data Fusion for Mobile Robot Navigation , 1995, IJCAI.

[29]  Zhiyu Xiang Environmental perception: an application of multi-sensor data fusion to autonomous off-road navigation , 2005, IEEE International Conference Mechatronics and Automation, 2005.

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

[31]  M. Teresa Escrig,et al.  Autonomous robot navigation using human spatial concepts , 2000, Int. J. Intell. Syst..

[32]  M. Egenhofer,et al.  Point-Set Topological Spatial Relations , 2001 .

[33]  Libor Pÿÿ Robust Data Fusion With Occupancy Grid , 2005 .

[34]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[35]  J. Hawkins,et al.  On Intelligence , 2004 .

[36]  Z. Vamossy,et al.  Environment mapping with laser-based and other sensors , 2004, International Workshop on Robot Sensing, 2004. ROSE 2004..

[37]  Christian Freksa,et al.  Using Orientation Information for Qualitative Spatial Reasoning , 1992, Spatio-Temporal Reasoning.

[38]  Lutz Frommberger,et al.  Spatial Abstraction: Aspectualization, Coarsening, and Conceptual Classification , 2008, Spatial Cognition.

[39]  Daniel Hernández,et al.  Relative representation of spatial knowledge: the 2-D case , 1990, Forschungsberichte, TU Munich.

[40]  Jean-Arcady Meyer,et al.  Visual topological SLAM and global localization , 2009, 2009 IEEE International Conference on Robotics and Automation.

[41]  S. Reece Data Fusion and Parameter Estimation Using Qualitative Models: The Qualitative Kalman Filter , 2003 .

[42]  António E. Ruano,et al.  Fast Line, Arc/Circle and Leg Detection from Laser Scan Data in a Player Driver , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[43]  Wilfried Brauer,et al.  Spatial Cognition III , 2003, Lecture Notes in Computer Science.

[44]  norm Dempster Qualitative model based multisensor data fusion and parameter estimation using norm Dempster Shafer evidential reasoning , 2015 .

[45]  Roland Siegwart,et al.  A cognitive modeling of space using fingerprints of places for mobile robot navigation , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[46]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[47]  Libor Preucil,et al.  Robust data fusion with occupancy grid , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[48]  Dídac Busquets,et al.  A Multiagent Approach to Qualitative Landmark-Based Navigation , 2003, Auton. Robots.

[49]  Erik Schaffernicht,et al.  Multi-modal sensor fusion using a probabilistic aggregation scheme for people detection and tracking , 2006, Robotics Auton. Syst..

[50]  R. Adams Proceedings , 1947 .

[51]  Juan Carlos Peris Broch,et al.  Cognitive Maps for Mobile Robot Navigation : A Hybrid Representation Using Reference Systems 1 , 2022 .

[52]  Lindsay Kleeman,et al.  Advanced sonar and laser range finder fusion for simultaneous localization and mapping , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[53]  Hugh F. Durrant-Whyte,et al.  Multisensor data fusion for underwater navigation , 2001, Robotics Auton. Syst..

[54]  Lindsay Kleeman,et al.  Interactive SLAM using Laser and Advanced Sonar , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[55]  David Filliat,et al.  Incremental topo-metric SLAM using vision and robot odometry , 2011, 2011 IEEE International Conference on Robotics and Automation.