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Indoor/campus positioning systems serve as the basis of a broad category of context-aware applications. In this paper, the authors present a novel indoor/campus positioning/tracking framework, which integrates WLAN based positioning methods and low-cost MEMS sensors, e.g. pressure and acceleration sensors, as well as semantic information. By using MEMS sensors, user state information, e.g. walking distance and altitude, can be inferred to compensate the weakness of WLAN positioning systems. Further on, semantic information extracted from maps can be used to improve the tracking of mobile devices. The authors' experiment shows that this hybrid framework can greatly improve accuracy and robustness of 3D indoor/campus localization.