Journal
REMOTE SENSING
Volume 11, Issue 13, Pages -Publisher
MDPI
DOI: 10.3390/rs11131535
Keywords
indoor mapping; room type tagging; semantic enrichment; grammar; Bayesian inference
Categories
Funding
- National Key Research and Development Program of China [2016YFB0502200]
- National Natural Science Foundation of China [41271440]
- China Scholarship Council
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Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging).
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