A Supervised Learning Approach to Robot Localization Using a Short-Range RFID Sensor

This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.

[1]  Nobuhiro Okada,et al.  Location-Driven Image Retrieval for Images Collected by a Mobile Robot , 2006 .

[2]  Bernhard Nebel,et al.  RFID Technology-based Exploration and SLAM for Search And Rescue , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Margrit Betke,et al.  Mobile robot localization using landmarks , 1997, IEEE Trans. Robotics Autom..

[4]  Clark F. Olson.  Selecting Landmarks for Localization in Natural Terrain , 2002, Auton. Robots.

[5]  Manuela M. Veloso,et al.  Sensor resetting localization for poorly modelled mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[6]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[7]  Gaurav S. Sukhatme,et al.  Localization for Mobile Robot Teams: A Maximum Likelihood Approach , 2001 .

[8]  Lina María Paz,et al.  Global localization in SLAM in bilinear time , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Hugh Durrant-Whyte,et al.  SLAM Updates Require Constant Time , 2002 .

[10]  Jake K. Aggarwal,et al.  Mobile robot self-location using model-image feature correspondence , 1996, IEEE Trans. Robotics Autom..

[11]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

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

[13]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[14]  Stephen R. Marsland,et al.  Sensory Anticipation for Autonomous Selection of Robot Landmarks , 2003, ABiALS.

[15]  Roland Siegwart,et al.  Towards Feature-Based Multi-Hypothesis Localization and Tracking , 2001 .

[16]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[17]  Peter Wall,et al.  Mobile robot navigation using self-similar landmarks , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[18]  Patric Jensfelt,et al.  Feature based CONDENSATION for mobile robot localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[19]  Charles T. Jahren,et al.  RADIO-FREQUENCY IDENTIFICATION APPLICATIONS IN CONSTRUCTION INDUSTRY , 1995 .

[20]  Thomas J. Olson,et al.  View-invariant regions and mobile robot self-localization , 1996, IEEE Trans. Robotics Autom..

[21]  Sebastian Thrun,et al.  Bayesian Landmark Learning for Mobile Robot Localization , 1998, Machine Learning.

[22]  Jun Ota,et al.  Design of the Optimal Arrangement of Artificial Landmarks , 1996 .

[23]  Bruce A. MacDonald,et al.  Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison , 2005, IEEE Transactions on Robotics.

[24]  Paul Newman,et al.  SLAM-Loop Closing with Visually Salient Features , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[25]  Tamio Tanikawa,et al.  Ubiquitous Localization and Mapping for Robots with Ambient Intelligence , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Horst-Michael Groß,et al.  Vision-based Monte Carlo self-localization for a mobile service robot acting as shopping assistant in a home store , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Ingemar J. Cox,et al.  Underwater sonar data fusion using an efficient multiple hypothesis algorithm , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[28]  Fabio M. Marchese,et al.  A robot localization method based on evidence accumulation and multi-resolution , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Mike Schneider,et al.  Radio Frequency Identification (RFID) Technology and its Applications in the Commercial Construction Industry , 2003 .

[30]  Yoshihiko Kimuro,et al.  Self-localization of mobile robots with RFID system by using support vector machine , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[31]  E. Olson,et al.  Robust Range-Only Beacon Localization , 2004, IEEE Journal of Oceanic Engineering.

[32]  D. Kurth.  Range-Only Robot Localization and SLAM with Radio , 2004 .

[33]  Peter I. Corke,et al.  Further Results with Localization and Mapping Using Range from Radio , 2005, FSR.

[34]  Wolfram Burgard,et al.  Towards Lazy Data Association in SLAM , 2003, ISRR.

[35]  Gregory D. Hager,et al.  Real-time vision-based robot localization , 1993, IEEE Trans. Robotics Autom..

[36]  José A. Castellanos,et al.  Linear time vehicle relocation in SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[37]  Yoshiaki Shirai,et al.  Panoramic View-Based Navigation in Outdoor Environments Based on Support Vector Learning , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[38]  Axel Lankenau,et al.  Mobile robot self-localization in large-scale environments , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[39]  James J. Little,et al.  Vision-based mobile robot localization and mapping using scale-invariant features , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[40]  Horst Bischof,et al.  Mobile robot localization under varying illumination , 2002, Object recognition supported by user interaction for service robots.

[41]  Dieter Fox,et al.  An experimental comparison of localization methods continued , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[43]  E. Kondo,et al.  Vision-based self-localization in non-stationary environments by using support vector machines , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[44]  Hugh F. Durrant-Whyte,et al.  Mobile robot localization by tracking geometric beacons , 1991, IEEE Trans. Robotics Autom..

[45]  Danica Kragic,et al.  Integrating SLAM and Object Detection for Service Robot Tasks , 2005 .