Localisation remains a crucial aspect of robotic design. It forms the basis of any kind of autonomous navigation for drones, cars and other specialized robots. This is usually achieved using a Simultaneous Localisation and Mapping (SLAM) algorithm, which uses an input sensor to localise the robot within a map that is created simultaneously. The input sensors are either cameras, which provide visual data, or Light Detection And Ranging (LiDAR) sensors, which automatically deliver a point cloud up to surveying quality. In recent years, LiDAR inertial odometry (LIO) algorithms, which combine measurements from a LiDAR sensor and inertial measurements from an IMU, have become more popular. These algorithms do not use a previously recorded map, but rather create their own map during runtime. This paper contributes an improvement to the precision by integrating a predefined 3D global point cloud map into the localisation algorithm. Over the course of multiple experiments in different testing scenarios, we have achieved a 71% reduction of the distance error for localisation, while there was no significant change regarding the orientation error. This makes the presented system a suitable localisation option for real-world robotic operations at construction sites.