Hierarchical Modeling of Indoor Spaces: Towards Route Communication at Multiple Levels of Detail for Navigation in Buildings

A hierarchical data model is needed in mobile navigation systems to generate route instructions on multiple levels of detail (LODs), thereby adapting to users' various information needs during navigation. In complex multi-storey indoor environments, existing hierarchical data models mainly rely on logical graphs that represent indoor cellular spaces as nodes and adjacency as edges. Due to the lack of precise geometry, however, they have limited capability to support the accurate computation of walking distance and directions, which are essential in route instructions. This article proposes a hierarchical indoor visibility-based graph (HiVG) for navigation guidance in multi-storey buildings and presents a HiVG generation algorithm. The algorithm's input is an indoor visibility graph (iVG) in which the orientations of nodes to corridor areas are represented. In the algorithm, first the functions of edges in indoor route instructions are identified, after which an edge function-based graph partitioning iteration is performed to generate each level of the HiVG. Experiments with three buildings of different geometric configurations demonstrate the potential of our HiVG generation algorithm. Furthermore, the conducted case studies show that the proposed HiVG is appropriate for generating indoor route instructions on multiple LODs.

In mobile navigation systems, an appropriate level of detail of the route instructions provided is important for navigation users to understand, memorise, and follow routes. However, few existing indoor navigation systems are capable of providing route instructions with multiple levels of detail. To close this gap, it is critical to model indoor environments with multiple granularities for route instructions to be generated on varying levels of detail. We propose a hierarchical model for route instructions in multi-storey buildings by allowing for representing actions (i.e., turning left or right, and going straight) in conceptualising route instructions. As a proof of concept, a case study is being conducted to present the feasibility of the proposed hierarchical model.

For human-centered mobile navigation systems, a computational landmark selection model is critical to automatically include landmarks for communicating routes with users. Although some empirical studies have shown that landmarks selected by familiar and unfamiliar wayfinders, respectively, differ significantly, existing computational models are solely focused on unfamiliar users and ignore selecting landmarks for familiar users, particularly in indoor environments. Meanwhile, it is unclear how the importance of salience metrics employed by machine learning approaches differs from that reported by human participants during landmark selection. In this study, we propose a LambdaMART-based ranking approach to computationally modelling indoor landmark selection. Two models, one for familiar and one for unfamiliar users, respectively, were trained from the human-labelled indoor landmark selection data. The importance of different salience measures in each model was then ranked and compared with human participants' self-report results of a survey. The evaluation results demonstrate that familiarity does indeed matter in the computational modelling of indoor landmark selection. The ranking differences of salience measures in the trained models show that the salience varies with the familiarity of wayfinders. Moreover, the calculated intraclass correlation coefficients (0.62 for familiar, 0.65 for unfamiliar) illustrate the median consistency between the computational results on feature importance and the self-reported importance results by human participants, confirming the reliability and interpretability of the proposed approach.

When conveying information about routes to follow in complex environments, human route-givers adapt to route-receivers’ familiarity with the environments in their choice of landmarks. Meanwhile, as route-givers themselves have experienced the environments within a social role, the landmarks they select may also differ significantly. This research investigated how these two factors influence landmark selection when communicating routes in indoor environments. Two groups of participants were recruited to conduct indoor landmark selection experiments for familiar and unfamiliar route-receivers in a multi-functional university building. The results show an interaction effect between these factors in indoor landmark selection. These findings lay an empirical ground for developing human-centered mobile navigation systems that can adapt to users’ social roles and their familiarity with the environment.

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people's familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance.