Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Large-scale localization from wireless signal strength
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Map building without localization by dimensionality reduction techniques
Proceedings of the 24th international conference on Machine learning
Efficient Multi-robot Search for a Moving Target
International Journal of Robotics Research
Online co-localization in indoor wireless networks by dimension reduction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Adaptive localization in a dynamic WiFi environment through multi-view learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Transferring localization models across space
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Transferring localization models over time
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Transferring multi-device localization models using latent multi-task learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Centralized modeling of the communication space for spectral awareness in cognitive radio networks
ACM SIGMOBILE Mobile Computing and Communications Review
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Relative bearing estimation from commodity radios
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Indoor localization without the pain
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Autonomous construction of a WiFi access point map using multidimensional scaling
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Gaussian process occupancy maps*
International Journal of Robotics Research
Target tracking without line of sight using range from radio
Autonomous Robots
No need to war-drive: unsupervised indoor localization
Proceedings of the 10th international conference on Mobile systems, applications, and services
A unifying probabilistic perspective for spectral dimensionality reduction: insights and new models
The Journal of Machine Learning Research
Locating in fingerprint space: wireless indoor localization with little human intervention
Proceedings of the 18th annual international conference on Mobile computing and networking
Walkie-Markie: indoor pathway mapping made easy
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Gaussian Process Gauss-Newton for non-parametric simultaneous localization and mapping
International Journal of Robotics Research
CINA - A Crowdsourced Indoor Navigation Assistant
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.