Paper Info
Title
iMap: Automatic inference of indoor semantics exploiting opportunistic smartphone sensing
Abstract
Indoor environment inference is of great importance to mobile and pervasive computing. As high-level metadata of indoor environment, floor maps contain rich information and are widely required in many pervasive systems. However, despite significant research progress, automatic inference of indoor maps has been less studied. In this paper, we present iMap, a smartphone-based opportunistic sensing system that automatically constructs the indoor maps by merging crowdsourced walking trajectories from smart-phone users. Most importantly, indoor semantics, such as stairs, escalators, elevators and doors are also automatically detected and annotated to the constructed map in the same inference process. The evaluation result shows that iMap can accurately detect different indoor semantics and be applied to different indoor environments. With the capability of generating semantic-annotated indoor maps without requiring any prior knowledge of the indoor environment, iMap has the potential to be widely deployed in practice.
Year
DOI
Venue
2015
10.1109/SAHCN.2015.7338350
2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
Keywords
Field
DocType
iMap,automatic inference,indoor semantics,opportunistic smartphone sensing,indoor environment inference,pervasive computing,mobile computing,high-level metadata,indoor environment,pervasive systems,smartphone-based opportunistic sensing system,smartphone users,indoor semantics,semantic-annotated indoor maps
Mobile computing,Metadata,Computer science,Inference,Elevator,Human–computer interaction,Automatic inference,Ubiquitous computing,Semantics,Trajectory,Distributed computing
Conference
Citations 
PageRank 
References 
5
0.43
23
Authors
5
Name
Order
Citations
PageRank
Chengwen Luo119321.49
Hande Hong2455.73
Long Cheng324618.23
Kartik Sankaran4392.01
Mun Choon Chan5113097.54