Range-Only Robot Localization and SLAM with Radio

This paper presents three localization algorithms using radio beacons that provide the ability to measure range only. Obtaining range from radio has the advantage that line of sight between the beacons and the transponder is not required, and the data association problem can be completely avoided. An extended Kalman filter, a particle filter, and a nonlinear sliding batch method are used to fuse range data with dead reckoning data collected from a real system, and results are demonstrated. The extended Kalman filter is also applied to the SLAM problem.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

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

[4]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[5]  James L. Crowley,et al.  Position estimation for a mobile robot using vision and odometry , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[6]  Andy Hopper,et al.  A new location technique for the active office , 1997, IEEE Wirel. Commun..

[7]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[8]  J. Werb,et al.  Designing a positioning system for finding things and people indoors , 1998 .

[9]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[10]  Luis Moreno,et al.  Global Integration of Ultrasonic Sensors Information in Mobile Robot Localization , 1999 .

[11]  A. Topor.  Reliable Self-localization, Multirobot Sensor Integration, Accurate Path-planning and Basic Soccer Skills: Playing an Eeective Game of Robotic Soccer , 1999 .

[12]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[13]  Philip F. McLauchlan,et al.  A batch/recursive algorithm for 3D scene reconstruction , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[14]  Gaetano Borriello,et al.  SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength , 2000 .

[15]  P. Hartana,et al.  Sensor data fusion using Kalman filter , 2000, Proceedings of the Third International Conference on Information Fusion.

[16]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[17]  Seth J. Teller,et al.  The cricket compass for context-aware mobile applications , 2001, MobiCom '01.

[18]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

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

[20]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[21]  Matthew Deans,et al.  Maximally informative statistics for localization and mapping , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[22]  Sanjiv Singh,et al.  Preliminary results in range-only localization and mapping , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[23]  John J. Leonard,et al.  Pure range-only sub-sea SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[24]  Sanjiv Singh,et al.  Experimental results in range-only localization with radio , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[25]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[26]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[27]  Mobile Computing and Networking , .