Research has found that many people have problems finding their way within public buildings, such as airport, hospitals, offices or university buildings. Indoor navigation systems are specifically designed to assist people’s wayfinding tasks in such indoor environments. Current indoor navigation systems only provide users with shortest or fastest routes, even though humans rarely move around using only these criteria. As such, developing better, more adjusted indoor navigation systems could ease the wayfinding task in the indoor environment. This research will focus on the route planning aspect of these systems. Developing more appropriate wayfinding algorithms taking into account other criteria than travel distance and time for defining routes to support indoor navigation systems could be a novel contribution in this area. These cognitive algorithms will improve indoor navigation systems by guiding people along a more intuitive and easier-to-follow path.  The development of such algorithms entails the identification of those more intuitive path characteristics in the indoor environments. Subsequently, the implementation of these path characteristics in cognitive route planning algorithms require a theoretical conceptualization. These conceptualizations and algorithmic calculations are influenced by the underlying spatial model and therefore this aspect will be elucidated.