Experiments in Monte-Carlo Localization using WiFi Signal Strength

We study the problem of globally localizing a mobile robot in a structured environment given an 802.11b card on the robot, one or more 802.11b access points (APs) in the environment and a signal-strength map. The method for acquiring this map is described. A particle filter-based algorithm is implemented with an odometry-based action model and an observation model based on the signalstrength map. Tests carried out using a Pioneer 2DX robot with a wireless card show promising results. Localization is accurate a significant percentage of the time. We test the algorithm with one, two and three APs for observations and show empirically that improved results are obtained with a higher number of APs.

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

[2]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[3]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[4]  Anit Chakraborty,et al.  A distributed architecture for mobile, location-dependent applications , 2000 .

[5]  Wolfram Burgard,et al.  Particle Filters for Mobile Robot Localization , 2001, Sequential Monte Carlo Methods in Practice.

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

[7]  Aleksandar Neskovic,et al.  Modern approaches in modeling of mobile radio systems propagation environment , 2000, IEEE Communications Surveys & Tutorials.

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

[9]  H. Hashemi,et al.  The indoor radio propagation channel , 1993, Proc. IEEE.

[10]  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).

[11]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[12]  Kostas E. Bekris,et al.  Robotics-Based Location Sensing Using Wireless Ethernet , 2002, MobiCom '02.