Cognitive mapping of indoor environments: constructing an indoor navigation network from crowdsourced indoor route descriptions

ABSTRACT

Indoor navigation networks provide an effective solution for modeling indoor environments for navigational purposes. In comparison to pure geometric structures, network structures that are aligned with the user’s cognitive map are perceived as more intuitive. However, there has been limited research conducted on constructing indoor navigation networks that align with user’s cognitive maps. Human route descriptions contain valuable insights into how individuals recognize their surroundings and how humans structure spatial information cognitively. Using crowdsourced route descriptions to construct indoor navigation networks can provide an approximation of people’s cognitive maps. This study proposes a graph-based method to solve the problem of merging human indoor route descriptions. It models indoor route descriptions as directed attributed graphs and transforms the challenges into a graph merging problem. This method relies on order and direction information between references. In a case study with human indoor route descriptions, an indoor navigation network was generated by merging crowdsourced indoor route descriptions. The network has the potential to facilitate a comparison between existing indoor network models and identify the indoor navigation network that best matches the network derived from crowdsourced route descriptions. Additionally, the network could be used to generate more intuitive indoor route descriptions.

KEYWORDS:

• Indoor navigation network
• indoor route descriptions
• graph matching
• crowdsourced navigation system
• route graph

Acknowledgments

The authors would like to thank colleagues at the Building S8, Ghent University, for contributing their route descriptions. The authors would like to thank the editor and the anonymous reviewers for their valuable comments.

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available with the identifier at the private link https://osf.io/s4nqc/?view_only=e39001b883834544bc5cdc317004eccb.

Additional information

Funding

The authors would like to thank China Scholarship Council for their support [Grant No. 201906040236].