Recent advances in technology, in particular the miniaturization and commoditization of electronic wireless receivers, have enabled new applications and techniques for wireless communication, mapping, and localization.

The example we provide is focused on GNSS (Global Navigation Satellite System) receivers, which widely deployed in mobile consumer electronics for positioning, can be also be leveraged as environment sensors. The problem of estimating a 3D map, or a world model, via crowd-sourced wireless signal strength measurements from global positioning satellites, is explored in detail. The inverse problem (localizing the receiver against a known or partially known 3D map) is then discussed, as is the fully integrated simultaneous localization and mapping (SLAM) problem where both the trajectory of the GNSS receiver and 3D map are jointly estimated. For all of of these cases, novel probabilistic modeling approaches of the map and receiver location, as well as the channel model, are introduced. The proposed solutions are validated using real-world data from consumer grade mobile devices (smart phones) on both small and larger (multi-city block scale) experiments. For positioning, the backend positioning server and associated smartphone app were prototyped in software, with improved localization using a particle filter being demonstrated in real-time.