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Magnetic field sensing for pedestrian and robot indoor positioning
at
| Leonid Antsfeld, Boris Chidlovskii |
| 11th International Conference on Indoor Positioning and Indoor Navigation, Lloret de Mar, Spain, 29 November - 2 December, 2021 |
In this paper we address the problem of indoor localization using magnetic field data in two setups, when data is collected by (i) a human-held mobile phone and (ii) by localization robots that perturb magnetic data with their own electromagnetic field. For the first setup, we revise the state of the art approaches and propose an extended pipeline to benefit from the presence of magnetic anomalies in indoor environments created by different ferromagnetic objects. We capture changes of the Earth’s magnetic field due to indoor magnetic anomalies and transform them into multi-variate times series. We then propose to convert temporal patterns into visual ones. We use methods of Recurrence Plots, Gramian Angular Fields and Markov Transition Fields to represent magnetic field time series as image sequences. We regress continuous user position by combining convolutional and recurrent layers in deep neural networks. For the second setup, we analyse how magnetic field data get perturbed by robots’ electromagnetic field. We propose an alignment step to compensate the mismatch between train and test sets obtained by different robots. We test our methods on two public datasets (MagPie [11] and IPIN’20 [4]) and one proprietary (Hyundai department store). We report evaluation results and show that our methods outperform the state of the art methods by a large margin.
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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