Combining classification and regression for WiFi localization of heterogeneous robot teams in unknown environments

Abstract

We consider the problem of team-based robot mapping and localization using wireless signals broadcast from access points embedded in today's urban environments. We map and localize in an unknown environment, where the access points' locations are unspecified and for which training data is a priori unavailable. Our approach is based on an heterogeneous method combining robots with different sensor payloads. The algorithmic design assumes the ability of producing a map in real-time from a sensor-full robot that can quickly be shared by sensor-deprived robot team members. More specifically, we cast WiFi localization as classification and regression problems that we subsequently solve using machine learning techniques. In order to produce a robust system, we take advantage of the spatial and temporal information inherent in robot motion by running Monte Carlo Localization on top of our regression algorithm, greatly improving its effectiveness. A significant amount of experiments are performed and presented to prove the accuracy, effectiveness, and practicality of the algorithm.