The active badge location system
ACM Transactions on Information Systems (TOIS)
Tracking requirements for augmented reality
Communications of the ACM - Special issue on computer augmented environments: back to the real world
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Multivariate Analysis for Probabilistic WLAN Location Determination Systems
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
PinPoint: An Asynchronous Time-Based Location Determination System
Proceedings of the 4th international conference on Mobile systems, applications and services
Pedestrian localisation for indoor environments
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Growing an organic indoor location system
Proceedings of the 8th international conference on Mobile systems, applications, and services
Indoor localization without the pain
Proceedings of the sixteenth annual international conference on Mobile computing and networking
An algorithmic framework for segmenting trajectories based on spatio-temporal criteria
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Wi-Fi Fingerprint-Based Topological Map Building for Indoor User Tracking
RTCSA '10 Proceedings of the 2010 IEEE 16th International Conference on Embedded and Real-Time Computing Systems and Applications
GDC: Group Discovery Using Co-location Traces
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
3D building roof reconstruction from point clouds via generative models
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
On the shape of a set of points in the plane
IEEE Transactions on Information Theory
No need to war-drive: unsupervised indoor localization
Proceedings of the 10th international conference on Mobile systems, applications, and services
Hallway based automatic indoor floorplan construction using room fingerprints
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Dejavu: an accurate energy-efficient outdoor localization system
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hi-index | 0.00 |
The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment. Implementation of the system in two testbeds, using different Android phones, shows that CrowdInside can detect the points of interest accurately with 0.2% false positive rate and 1.3% false negative rate. In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users of the building. We also discuss possible extensions to CrowdInside for inferring even higher level semantics about the discovered floorplans.