Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries

ISPRS International Journal of Geo-Information

Tianyue Liu¹  Si Chen²  Ling Peng³  Tianhe Chi³  Hui Lin³ 

[1] Beijing Jinghang Computation and Communication Research Institute, Beijing 100074, China;Department of Computer Science, University of Massachusetts Boston, Boston, MA 02125, USA;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, CAS Olympic S & T Park, No. 20 Datun Road, P.O. Box 9718, Beijing 100101, China;

关键词: indoor adjacency matrix;    indoor complex semantic query;    semantic trajectory;    indexing for indoor moving objects;   

DOI  :  10.3390/ijgi5100176

来源: DOAJ

【 摘 要 】

With the emergence of various types of indoor positioning technologies (e.g., radio-frequency identification, Wi-Fi, and iBeacon), how to rapidly retrieve indoor cells and moving objects has become a key factor that limits those indoor applications. Euclidean distance-based indexing techniques for outdoor moving objects cannot be used in indoor spaces due to the existence of indoor obstructions (e.g., walls). In addition, currently, the indexing of indoor moving objects is mainly based on space-related query and less frequently on semantic query. To address these two issues, the present study proposes a multi-floor adjacency cell and semantic-based index (MACSI). By integrating the indoor cellular space with the semantic space, the MACSI subdivides open cells (e.g., hallways and lobbies) using space syntax and optimizes the adjacency distances between three-dimensionally connected cells (e.g., elevators and stairs) based on the caloric cost that extends single floor indoor space to three dimensional indoor space. Moreover, based on the needs of semantic query, this study also proposes a multi-granularity indoor semantic hierarchy tree and establishes semantic trajectories. Extensive simulation and real-data experiments show that—compared with the indoor trajectories delta tree (ITD-tree) and the semantic-based index (SI)—the MACSI produces more reliable query results with significantly higher semantic query and update efficiencies; has superior semantic expansion capability; and supports multi-granularity complex semantic queries.

【 授权许可】

Unknown