In recent years, pedestrian navigation assistance has been used by an increasing number of people to support wayfinding tasks. Especially in unfamiliar and complex indoor environments such as universities and hospitals, the importance of an effective navigation assistance becomes apparent. This paper investigates the feasibility of the indoor landmark navigation model (ILNM) [1], a method for generating landmark-based routing instructions, by combining it with indoor route maps and conducting a wayfinding experiment with human participants. Within this context, three different cartographic visualization scenarios were designed and evaluated. Two of these scenarios were based on the implementation of the ILNM algorithm, with the concurrent effort to overcome the challenge of representing the semantic navigation instructions in two different ways. In the first scenario, the selected landmarks were visualized as pictograms, while in the second scenario, an axonometric-based design philosophy for the depiction of landmarks was followed. The third scenario was based on the benchmark approach (metric-based routing instructions) for conveying routing instructions to the users. The experiment showed that the implementation of the ILNM was feasible, and, more importantly, it was beneficial in terms of participants’ navigation performance during the wayfinding experiment, compared to the metric-based instructions scenario (benchmark for indoor navigation). Valuable results were also obtained, concerning the most suitable cartographic approach for visualizing the selected landmarks, while implementing this specific algorithm (ILNM). Finally, our findings confirm that the existence of landmarks, not only within the routing instructions, but also as cartographic representations on the route map itself, can significantly help users to position themselves correctly within an unfamiliar environment and to improve their navigation performance.
OUTDOOR and INDOOR VISION BASED LOCALIZATION FOR BLIND PEDESTRIAN NAVIGATION ASSISTANCE
