Over the past 15 years, there has been an exponential growth of new technologies for indoor positioning and navigation. Unlike GNSS technology, which has become the leading solution for outdoor positioning, no technology has taken the lead indoors. With the price drop of radio beacons, we see a massive deployment of beacons' networks for positioning. Image processing is progressing fast thanks to machine learning techniques that improve the rendering of very low-cost cameras. More and more smart devices embed inertial sensors providing autonomous navigation options. These are only a few of the technologies deployed. Hybridizing technologies to find the best compromise between accuracy, cost and energy consumption is at the heart of ongoing development. The nature of sensors in the infrastructure or in smart devices, specific use cases requirements and privacy concerns about geolocated data are all features used to choose the right technologies to hybridize. Adopting a ubiquitous approach that combines dead reckoning and absolute positioning while recognizing the application and environmental context is certainly a strong trend in current developments and research. Given the great diversity of existing positioning systems and ways of presenting their performance, it seems almost impossible to provide a clear comparison of localization performance. The key to this comparison certainly lies in experimental comparative trials, in the same context and on identical scenarios. This approach started several years ago with international positioning competitions. This talk will review main indoor positioning technologies according to several comparison criteria. It will also exploit the results of last Indoor Positioning Indoor Navigation (IPIN) international competition that took place in a 9'000 m² shopping mall (Atlantis) in Nantes (France) where 49 teams competed.