Identifying indoor navigation landmarks using a hierarchical multi-criteria decision framework

Landmarks play a vital role in human wayfinding by providing the structure for mental spatial representations and indicating locations with which to orient. Less research effort has been allocated towards automated landmark identification in indoor environments despite a growing interest in indoor navigation in the scientific community. In this paper, we propose a computational framework to identify indoor landmarks that is based on a hierarchical multi-criteria decision model and grounded in theories of spatial cognition and human information processing. Our model of landmark salience is represented as a hierarchical integration process of low-level features derived from a three-part, higher-level, salience vector (i.e., cognitive, spatial, and subjective salience). We use a fuzzy hierarchical composite-weighted (objective and subjective) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to derive the rankings for identified objects at decision points (i.e., intersections). The top N objects are then selected and compared to a list of landmarks derived from an eye-tracking based virtual reality (VR) experiment. A substantial overlap of 79% was observed between these two lists. The proposed framework is capable of reliably and accurately detecting indoor landmarks, which can be employed in the development of landmark-based robot/autonomous agent motion and indoor guidance systems