Automatic translation of human route descriptions into schematic maps for indoor navigation

ABSTRACT

People create route descriptions based on their mental maps to provide route guidance, which represents their knowledge of the environment. Recent studies have attempted to model navigation knowledge from human route descriptions to facilitate route communication. However, they mainly focus on outdoor environments and do not address the representation of human descriptions of indoors in the form of schematic maps through the automatic extraction of spatial knowledge. Schematic maps have been commonly applied for public transportation by utilizing abstract representations to reduce cognitive load. Compared to route descriptions, schematic maps can provide easy-to-understand navigation guidance. In this paper, we present a novel NLP-based pipeline to automatically generate schematic maps from human route descriptions for indoor navigation. The experimental data consists of a set of crowdsourced route descriptions that follow a common template for a test building of the Soleway indoor navigation web service. The route descriptions and the generated schematic maps were presented to human participants in an online survey, and it was found that 92% of the generated schematic maps matched well with the corresponding human route descriptions. Thus, the proposed method is an effective and reliable approach for modeling route descriptions through schematic maps in indoor route communication.

KEYWORDS:

• Indoor navigation
• human route description
• natural language processing
• NLP
• schematic map
• route guidance

Acknowledgments

The authors would like to thank TUBITAK (The Scientific and Technological Research Council of Turkey) for their support (number 2219-2020/1: 1059B192000681), research assistant Atakan Bilgili (Yildiz Technical University) for the survey study and all participants who took part in the survey.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and codes that support the findings of this study are openly available at https://doi.org/10.6084/m9.figshare.22032617.v1

Additional information

Funding

This research was funded by the TUBITAK (The Scientific and Technological Research Council of Turkey), grant number [2219-2020/1: 1059B192000681].