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Semi-supervised variational autoencoder for WiFi indoor localization
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| Boris Chidlovskii, Leonid Antsfeld |
| 10th International Conference on Indoor Positioning and Indoor Navigation (IPIN'19), Pisa, Italy, 30 September-3 October, 2019 |
| Download |
@inproceedings{chidlovskii2019semi,
title={Semi-supervised Variational Autoencoder for WiFi Indoor Localization},
author={Chidlovskii, Boris and Antsfeld, Leonid},
booktitle={2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages={1--8},
organization={IEEE}
}
We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model from a small set of annotated WiFi observations and a massive set of non-annotated WiFi observations. Our method is based on the variational autoencoder deep network. We complement the network with an additional component of structural projection able to further improve the localization accuracy in a complex, multi-building and multi-floor environment. We consider several different network compositions which combine the classification and regression sub-tasks to achieve the optimal performance. We evaluate our method on the public UJI-IndoorLoc dataset and show that the proposed method allows to maintain the state of the art localization accuracy with a very limited amount of annotated data.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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