Many indoor localization techniques have been widely investigated. As the proportion of people with multiple mobile devices is increasing, an interesting research challenge arising from this growth is how to integrate data from multiple devices carried by a mobile user for localizing her/him. This work designs amulti-device joint localizationsystem, calledJoLo, to realize the idea using a group of off-the-shelf mobile devices carried by a user. The proposed system collects wireless fingerprints at the predefined training locations in the environment. Then, the system fuses the real-time wireless measurements observed by the multiple devices of a user into a summary list for localization reference. Finally, three fusion-based positioning algorithms are proposed to determine the user's location based on the fused summary list. The three proposed algorithms reduce mean distance errors in localization to less than one meter in a lab environment. One of the fusion-based algorithms, namely cluster-based DF algorithm, can even improve the performance by 50% on average compared to the existing single-device localization techniques.