Autonomous Robots

, Volume 39, Issue 2, pp 155–167 | Cite as

Real-time WiFi localization of heterogeneous robot teams using an online random forest

  • Balaguer Benjamin
  • Gorkem Erinc
  • Stefano Carpin


In this paper we present a WiFi-based solution to the localization and mapping problem for teams of heterogeneous robots operating in unknown environments. By exploiting wireless signal strengths broadcast from access points, a robot with a large sensor payload creates a WiFi signal map that can then be shared and utilized for localization by sensor-deprived robots. In our approach, WiFi localization is cast as a classification problem. An online clustering algorithm processes incoming WiFi signals that are then incorporated into an online random forest (ORF). The algorithm’s robustness is increased by a Monte Carlo localization algorithm whose sensor model exploits the results of the ORF classification. The proposed algorithm is shown to run in real-time, allowing the robots to operate in completely unknown environments, where a priori information such as a blue-print or the access points’ location is unavailable. A comprehensive set of experiments not only compares our approach with other algorithms, but also validates the results across different scenarios covering both indoor and outdoor environments.


Localization Multi-robot systems WiFi Machine learning 


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Balaguer Benjamin
    • 1
  • Gorkem Erinc
    • 1
  • Stefano Carpin
    • 2
  1. 1.Intelligent Computer Programming Labs Inc.Rio VistaUSA
  2. 2.School of EngineeringUniversity of California, MercedMercedUSA

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