Localization in 5G networks perfectly illustrates how cellular networks strive to handle all wireless use cases. However, despite the growing interest around indoor localization, the complex radio environment along with the lack of standard-compliant equipment make positioning in 5G networks a daunting task. Although some previous works have proposed experimental solutions, none has ever tackled a comprehensive scheme based on real-life 5G networks. Therefore, the purpose of our paper is to address the most exhaustive solution, taking advantage of all the possibilities offered today by the 5G ecosystem. This new vision emphasizes efficiency, practicality, and offers current 5G networks a localization service with a precision of around 3 meters. To do so, an analysis of all possible localization methods is conducted, retaining only those deemed satisfactory, namely fingerprinting and Pedestrian Dead Reckoning (PDR). Subsequently, we combine these two solutions, where the shortcomings of one are compensated by the strengths of the other. Through extensive experiments, we demonstrate that our PRILUN algorithm outperforms fingerprinting, PDR, and other similar approaches. Extensive real-life experiments in our 5G infrastructure highlight the performance expected to date in a typical 5G network. This paper therefore paves the way for the most comprehensive indoor localization solution in current 5G networks.