This paper presents a wearable positioning system which is able to provide real-time positioning information for all kinds of environments in a robust way. The system is mainly based on a foot-mounted INS assisted by a GNSS receiver as well as a barometer. The scenario presented in this paper took place during the final competition of the challenge MALIN (MAîtrise de la Localisation INdoor) organized by the DGA (Direction Générale de l'Armement) and the French National Research Agency (ANR). The objective of this challenge is to create a positioning system to track emergency response agents in non-collaborative environments, where GNSS signals are usually defeated. The proposed INS-based foot-mounted system is able to provide highly accurate positioning for various motion types (walking, running, stairs, ladder) thanks to a machine learning-based Zero Velocity Detector (ZVD). The external GNSS receiver is used to capture the GNSS positions in favorable conditions and further to provide absolute position and orientation corrections via a least square-based point pattern matching algorithm. The proposed system is tested over a 2.5 km trajectory in a soldier scenario including complex outdoor and deep indoor environments. The proposed system was able to provide real-time positioning information with an accuracy around 0.3% of error over the total traveled distance. The 75% HPE remains below 8 m and the 75% VPE is under 3 m.