The real-time position and mobility of a user is key to providing personalized location-based services (LBSs) – such as navigation. With the pervasiveness of GPS-enabled mobile devices (MDs), LBSs in outdoor environments is common and effective. However, providing equivalent quality of LBSs using GPS in indoor environments can be problematic. The ubiquity of both WiFi in indoor environments and WiFi-enabled MDs, makes WiFi a promising alternative to GPS for indoor LBSs. The most promising approach to establishing a WiFi-based indoor positioning system requires the construction of a high quality radio map for an indoor environment. However, the conventional approach for making the radio map is labor intensive, time-consuming, and vulnerable to temporal and environmental dynamics. To address this situation, researchers at UC Berkeley developed an approach for automatic, fine-grained radio map construction and adaptation. The Berkeley technology works both (a) in free space – where people and robots can move freely (e.g. corridors and open office space); and (b) in constrained space – which is blocked or not readily accessible. In addition to its use with WiFi signals, this technology could also be used with other RF signals – for example, in densely populated and built-up urban areas where it can be suboptimal to only rely on GPS.