“Turn Left after the WC, and Use the Lift to Go to the 2nd Floor”—Generation of Landmark-Based Route Instructions for Indoor Navigation

Abstract

People in unfamiliar environments often need navigation guidance to reach a destination. Research has found that compared to outdoors, people tend to lose orientation much more easily within complex buildings, such as university buildings and hospitals. This paper proposes a category-based method to generate landmark-based route instructions to support people's wayfinding activities in unfamiliar indoor environments. Compared to other methods relying on detailed instance-level data about the visual, semantic, and structural characteristics of individual spatial objects, the proposed method relies on commonly available data about categories of spatial objects, which exist in most indoor spatial databases. With this, instructions like "Turn right after the second door, and use the elevator to go to the second floor" can be generated for indoor navigation. A case study with a university campus shows that the method is feasible in generating landmark-based route instructions for indoor navigation. More importantly, compared to metric-based instructions (i.e., the benchmark for indoor navigation), the generated landmark-based instructions can help users to unambiguously identify the correct decision point where a change of direction is needed, as well as offer information for the users to confirm that they are on the right way to the destination.

Cartographic Visualization for Indoor Semantic Wayfinding

“…The indoor landmark navigation model (ILNM) is an algorithm for generating landmark-based route instructions to facilitate wayfinding activities in indoor environments [11]. Similar to the method proposed by Duckham et al [10], this specific approach is not based on detailed instance-level data about the visual, semantic, and structural characteristics of individual spatial objects that may be used as landmarks.…”

“…Along this route lie many spatial objects that may serve as landmarks. This element was quite crucial in selecting this specific path, since it was quite representative and suitable for applying the proposed algorithm [11]. ii.…”

“…The scoring of the suitability for every spatial feature was based on the system proposed in Reference [11] (Figure 13). Below, in Table 1, an example rating for the spatial feature category of door is presented: The ratings above (Table 1) were then combined to generate an overall suitability score for the feature category "door", using the scoring system ( Figure 13) proposed by Reference [11]. More specifically, the following score is obtained: -"Physical size" = 8, since it is rated as "Ideal" and as "All" in terms of suitability and typicality, respectively; -"Prominence" = 4, since it is rated as "Highly suitable" and as "Most" in terms of suit-ability and typicality, respectively; -"Difference from surroundings" = 4, since it is rated as "Highly suitable" and as "Most" in terms of suitability and typicality, respectively; -"Availability of a unique label" = 0, since it is rated as "Never suitable" and as "All" in terms of suitability and typicality, respectively; -"Ubiquity and familiarity" = 8, since it is rated as "Ideal" and as "All" in terms of suitability and typicality, respectively; -"Length of description" = 8, since it is rated as "Ideal" and as "All" in terms of suitability and typicality, respectively; -"Spatial extents" = 4, since it is rated as "Highly suitable" and as "Most" in terms of suitability and typicality, respectively; -"Permanence" = 8, since it is rated as "Ideal" and as "All" in terms of suitability and typicality, respectively.…”

“…In Duckham et al[10] a category-based method for generating routing instructions based on landmarks was introduced. Although this approach aims on outdoor navigation, Fellner and her colleagues based their work on it and expanded it by developing an algorithm, the indoor landmark navigation Model, for an indoor navigation scenario [11].This article aims to investigate the feasibility of the ILNM [11], by conducting a wayfinding experiment with human participants, as well as to combine it with indoor route maps and evaluate three different cartographic visualization scenarios.The rest of the article is organized as follows. Section 2 presents related work.…”

“…[10] a category-based method for generating routing instructions based on landmarks was introduced. Although this approach aims on outdoor navigation, Fellner and her colleagues based their work on it and expanded it by developing an algorithm, the indoor landmark navigation Model, for an indoor navigation scenario [11].…”