A diversified generative latent variable model for WiFi-SLAM

WiFi-SLAM aims to map WiFi signals within an unknown environment while simultaneously determining the location of a mobile device. This localization method has been extensively used in indoor, space, undersea, and underground environments. For the sake of accuracy, most methods label the signal readings against ground truth locations. However, this is impractical in large environments, where it is hard to collect and maintain the data. Some methods use latent variable models to generate latent-space locations of signal strength data, an advantage being that no prior labeling of signal strength readings and their physical locations is required. However, the generated latent variables cannot cover all wireless signal locations and WiFi-SLAM performance is significantly degraded. Here we propose the diversified generative latent variable model (DGLVM) to overcome these limitations. By building a positive-definite kernel function, a diversity-encouraging prior is introduced to render the generated latent variables non-overlapping, thus capturing more wireless signal measurements characteristics. The defined objective function is then solved by variational inference. Our experiments illustrate that the method performs WiFi localization more accurately than other label-free methods.