Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic

GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction

This paper presents a 3D lidar SLAM system based on improved regionalized Gaussian process (GP) map reconstruction to provide both low-drift state estimation and mapping in real-time for robotics applications. We utilize spatial GP regression to model the environment. This tool enables us to recover surfaces including those in sparsely scanned areas and obtain uniform samples with uncertainty. Those properties facilitate robust data association and map updating in our scan-to-map registration scheme, especially when working with sparse range data. Compared with previous GP-SLAM, this work overcomes the prohibitive computational complexity of GP and redesigns the registration strategy to meet the accuracy requirements in 3D scenarios. For large-scale tasks, a two-thread framework is employed to suppress the drift further. Aerial and ground-based experiments demonstrate that our method allows robust odometry and precise mapping in real-time. It also outperforms the state-of-the-art lidar SLAM systems in our tests with light-weight sensors.Comment: Accepted by IROS 202

Minimization of measuring points for the electric field exposure map generation in indoor environments by means of Kriging interpolation and selective sampling

In a world with increasing systems accessing to radio spectrum, the concern for exposure to electromagnetic fields is growing and therefore it is necessary to check limits in those areas where electromagnetic sources are working. Therefore, radio and exposure maps are continuously being generated, mainly in outdoor areas, by using many interpolation techniques. In this work, Surfer software and Kriging interpolation have been used for the first time to generate an indoor exposure map. A regular measuring mesh has been generated. Elimination of Less Significant Points (ELSP) and Geometrical Elimination of Neighbors (GEN) strategies to reduce the measuring points have been presented and evaluated. Both strategies have been compared to the map generated with all the measurements by calculating the root mean square and mean absolute errors. Results indicate that ELSP method can reduce up to 70% of the mesh measuring points while producing similar exposure maps to the one generated with all the measuring points. GEN, however, produces distorted maps and much higher error indicators even for 50% of eliminated measuring points. As a conclusion, a procedure for reducing the measuring points to generate radio and exposure maps is proposed based on the ELSP method and the Kriging interpolation.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Ultrasound Orientation Sensor

Ultrasound (US) is a painless method of gaining a visual representation of the internal structures of a human body. It is used to look for diseases and other abnormalities. In effort to minimize and eliminate the amount of error generated by the operation of an US machine, a team of WPI students conducted research into the causes and reasons as to why these problems are not resolved. Ultimately, the team approached the problem through the use of an inertial measurement unit (IMU), and the development of a graphical user interface to track the orientation of an US probe. The results supported that feedback regarding probe orientation can increase the ability to reproduce ultrasound images

Object Shape Classification Utilizing Magnetic Field Disturbance and Supervised Machine Learning

Various narrow artificial intelligence architectures are on the rise due to the development of Graphics Processing Units and, thus, computational capabilities. Massive number multiplication capabilities of GPUs enabled researches to create more complicated and advanced algorithms. Initially, a gaming hardware became a base for modern time Industrial Revolution. Machine learning, once a forgotten branch of computer science, attracts huge investments and interest. In 2014, Google acquired an UK-based start-up Deep Mind for over £400M. In 2016 Volkswagen invested $680M in autonomous vehicle and cyber security start-ups (1). Same year Microsoft announced a newly created AI fund (2) and in May this year it resulted in investment of$7.6M in Bonsai, an AI start-ups that hopes to help companies to integrate machine learning in the infrastructure (3). It seems that almost never-ending pockets of investors are motivated by a promise of automation of difficult tasks, which, until now, have never been performed by humans. This thesis explores various supervised machine learning algorithms, beginning with the simplest k-Nearest Neighbours and Multi-layer Perceptron, to the state of the art architecture created by the industry experts (Deep Residual Network from Microsoft Research), and prominent academic figures (i.e. GG from Oxford). Furthermore, the author of the thesis proposes two additional network structures, named Deep Inception and Stacked Artificial Residual Architecture, inspired by previously mentioned research

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors