WiFi localization and navigation for autonomous indoor mobile robots

Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We present the results of our exhaustive tests of the WiFi localization independently and in conjunction with the navigation of our custom-built mobile robot in extensive long autonomous runs.

[1]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

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

[3]  Polly Huang,et al.  Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics , 2005, MSWiM '05.

[4]  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).

[5]  Oscar Serrano Serrano,et al.  Robot localization using WiFi signal without intensity map , 2004 .

[6]  M.A. Sotelo,et al.  Indoor Robot Localization System Using WiFi Signal Measure and Minimizing Calibration Effort , 2005, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[7]  Manuela M. Veloso,et al.  RSS-based relative localization and tethering for moving robots in unknown environments , 2010, 2010 IEEE International Conference on Robotics and Automation.

[8]  J.-S. Gutmann,et al.  AMOS: comparison of scan matching approaches for self-localization in indoor environments , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

[9]  Gregory Dudek,et al.  Learning and evaluating visual features for pose estimation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[11]  Wolfram Burgard,et al.  Map learning and high-speed navigation in RHINO , 1998 .

[12]  Paul S. Bradley,et al.  Refining Initial Points for K-Means Clustering , 1998, ICML.

[13]  Dieter Fox,et al.  Gaussian Processes for Signal Strength-Based Location Estimation , 2006, Robotics: Science and Systems.

[14]  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).

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