WiFi localization, the task of determining the phys- ical location of a mobile device from wireless sig- nal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware appli- cations. However, most localization techniques re- quire a training set of signal strength readings la- beled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel tech- nique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GP- LVM) to determine the latent-space locations of un- labeled signal strength data. We show how GP- LVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topo- logical connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.