Capturing dynamic navigable space: an interactive semantic model to expand functional space for 3D indoor navigation

Human interaction with indoor objects constantly changes indoor space and its navigability. Spatial subdivision models that delineate navigable space become a crucial prerequisite for indoor navigation. However, existing spatial subdivision models do not fully capture the dynamic changes of the indoor context and have limitations in identifying precise navigable space, thereby reducing the accuracy and efficiency of indoor navigation. This study proposes a novel interactive semantic model (ISM) that consists of an interactive semantic base (ISB) and empirical rules to accurately determine the navigability of the reshaped functional spaces (F-Spaces) of indoor objects. First, two-level F-Spaces (fine-grained resource F-Space and coarse-grained structure F-Space) are defined to express multi-granularity interactive semantics for delineating heterogeneous F-Spaces. Second, empirical rules are established through an extensible multi-dimensional semantics classification framework to determine each F-Space's navigability. Lastly, a navigable F-Space generation scheme is designed by considering the adaptive navigability of the two-level F-Spaces. Simulated experiments show that the proposed model can generate precise and efficient dynamic navigable spaces. This study reduces the cognitive burden of human agents when facing indoor dynamic navigation, thereby improving the spatial experience of navigation.