Robot localization using WiFi signal without intensity map

This paper describes a method to estimate the position of a mobile robot in an indoor scenario using the odometric calculus and the WiFi energy received from the wireless infrastructure. This energy will be measured by wireless network card on-board a mobile robot, and it will be used as another regular sensor to improve position estimation. The Bayes rule will be used to accumulate localization probability as the robot moves on. In this paper several experiments in a university building are shown. The two major contributions of the presented work are that the self-localization error achieved is bounded, and that no significant degradation is observed when the theoretical WiFi energy at each point is taken from radio propagation model instead of an a priori experimental intensity map of the environment.

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

[2]  Gaurav S. Sukhatme,et al.  Experiments in Monte-Carlo Localization using WiFi Signal Strength , 2003 .

[3]  R. Simmons,et al.  Probabilistic Navigation in Partially Observable Environments , 1995 .

[4]  Eduard García Villegas,et al.  Localización en redes WLAN 802.11: desarrollo e implementación de una solución basada en traps SNMP , 2003 .

[5]  Daniel P. Siewiorek,et al.  Determining User Location For Context Aware Computing Through the Use of a Wireless LAN Infrastructure , 2000 .

[6]  Johann Borenstein,et al.  UMBmark : a method for measuring, comparing, and correcting dead-reckoning errors in mobile robots , 1994 .

[7]  Ingemar J. Cox,et al.  Autonomous Robot Vehicles , 1990, Springer New York.

[8]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[9]  E. A. Puente,et al.  A framework for the integration of perception and localization systems over mobile platforms , 1999 .

[10]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[11]  Andrew Clarke.  A Reaction Diffusion Model for Wireless Indoor Propagation , 2002 .

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

[13]  Sebastian Thrun,et al.  Probabilistic Algorithms in Robotics , 2000, AI Mag..

[14]  Liqiang Feng,et al.  Navigating Mobile Robots: Systems and Techniques , 1996 .

[15]  Wolfram Burgard,et al.  Integrating Topological and Metric Maps for Mobile Robot Navigation: A Statistical Approach , 1998, AAAI/IAAI.