Scene Recognition for Robot Localization in Difficult Environments

Scene understanding is still an important challenge in robotics. In this paper we analyze the utility of scene recognition to determine the localization of a robot. We assume that multi-sensor localization systems may be very useful in crowded environments where there will be many people around the robot but not many changes of the furniture. In our localization system we categorize the sensors in two groups: accurate sensor models able to determine the pose of the robot accurately but which are sensible to noise or the presence of people. Robust sensor modalities able to provide rough information about the pose of the robot in almost any condition. The performance of our localization strategy was analyzed through two experiments realized in the Centro Singular de Investigacion en Tecnoloxias da Informacion (CITIUS), at the University of Santiago de Compostela.

[1]  James M. Rehg,et al.  Visual place categorization , 2009 .

[2]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

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

[4]  Xose Manuel Pardo,et al.  Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments , 2013, Sensors.

[5]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[6]  Wolfram Burgard,et al.  Mapping and localization with RFID technology , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[7]  Xose Manuel Pardo,et al.  Scene Recognition Invariant to Symmetrical Reflections and Illumination Conditions in Robotics , 2015, IbPRIA.

[8]  Roberto Iglesias,et al.  Robust Multi-sensor System for Mobile Robot Localization , 2013, IWINAC.

[9]  Michael Drumheller,et al.  Mobile Robot Localization Using Sonar , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[11]  James M. Rehg,et al.  CENTRIST: A Visual Descriptor for Scene Categorization , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.