On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
The active badge location system
ACM Transactions on Information Systems (TOIS)
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
LANDMARC: indoor location sensing using active RFID
Wireless Networks - Special issue: Pervasive computing and communications
The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Accuracy characterization for metropolitan-scale Wi-Fi localization
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Aging in place: fall detection and localization in a distributed smart camera network
Proceedings of the 15th international conference on Multimedia
Pedestrian localisation for indoor environments
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Wireless device identification with radiometric signatures
Proceedings of the 14th ACM international conference on Mobile computing and networking
RF-Based Initialisation for Inertial Pedestrian Tracking
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors
Proceedings of the 11th international conference on Ubiquitous computing
Indoor localization without the pain
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Precise indoor localization using PHY layer information
Proceedings of the 10th ACM Workshop on Hot Topics in Networks
WiGEM: a learning-based approach for indoor localization
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
SpinLoc: spin once to know your location
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
No need to war-drive: unsupervised indoor localization
Proceedings of the 10th international conference on Mobile systems, applications, and services
Locating in fingerprint space: wireless indoor localization with little human intervention
Proceedings of the 18th annual international conference on Mobile computing and networking
FM-based indoor localization via automatic fingerprint DB construction and matching
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Avoiding multipath to revive inbuilding WiFi localization
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
ArrayTrack: a fine-grained indoor location system
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Walkie-Markie: indoor pathway mapping made easy
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Fair and resilient incentive tree mechanisms
Proceedings of the 2013 ACM symposium on Principles of distributed computing
Dude, where's my card?: RFID positioning that works with multipath and non-line of sight
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Walk detection and step counting on unconstrained smartphones
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
RF-compass: robot object manipulation using RFIDs
Proceedings of the 19th annual international conference on Mobile computing & networking
Social-Loc: improving indoor localization with social sensing
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
iMac: strategy-proof incentive mechanism for mobile crowdsourcing
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
LiveLabs: initial reflections on building a large-scale mobile behavioral experimentation testbed
ACM SIGMOBILE Mobile Computing and Communications Review
3D tracking via body radio reflections
NSDI'14 Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation
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Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.