Combined infrared-ultrasonic positioning system to improve the data availability

Many indoor positioning applications related to accurate monitoring and tracking targets require centimeter precision. Infrared (IR)- and ultrasound (US)-based systems represent a feasible approach providing high robustness against interference. Furthermore, their combination may achieve better performance by mitigating their complementary drawbacks, covering larger areas, and improving the availability of positioning measurements. In this context, this work presents the proposal and experimental evaluation of a tightly coupled fusion method that uses an extended Kalman filter (EKF) to merge an IR- and a US-based positioning system. An outlier detection method is considered to select measurements with an adequate performance. Experimental results reveal that the IR and US systems are unable to position in 4.08% and 26.06% of locations, whereas the combined IR -US system has 100% of availability. In addition, the merged solution achieves less than 4 cm of positioning error in 90% of cases, outperforming the IR and US systems when they work independently

Experimental Evaluation of a Machine Learning-Based RSS Localization Method Using Gaussian Processes and a Quadrant Photodiode

The research interest on indoor Location-Based Services (LBS) has increased during the last years, especially using LED lighting, since they can deal with the dual functionality of lighting and localization with centimetric accuracy. There are several positioning approaches using lateration and angular methods. These methods typically rely on the physical model to deal with the multipath effect, environmental fluctuations, calibration of the optical setup, etc. A recent approach is the use of Machine Learning (ML) techniques. ML techniques provide accurate location estimates based on observed data without requiring the underlying physical model to be described. This work proposes an optical indoor local positioning system based on multiple LEDs and a quadrant photodiode plus an aperture. Different frequencies are used to allow the simultaneous emission of all transmitted signals and their processing at the receiver. For that purpose, two algorithms are developed. First, a triangulation algorithm based on Angle of Arrival (AoA) measurements, which uses the Received Signal Strength (RSS) values from every LED on each quadrant to determine the image points projected from each emitter on the receiver and, then, implements a Least Squares Estimator (LSE) and trigonometric considerations to estimate the receiver?s position. Secondly, the performance of a data-driven approach using Gaussian Processes is evaluated. The proposals have been experimentally validated in an area of 3 × 3m2 and a height of 1.3m (distance from transmitters to receiver). The experimental tests achieve p50 and p95 2D absolute errors below 9.38 cm and 21.94 cm for the AoA-based triangulation algorithm, and 3.62 cm and 16.65 cm for the Gaussian Processes.Agencia Estatal de Investigació

Accuracy and precision of agents orientation in an indoor positioning system using multiple infrastructure lighting spotlights and a PSD sensor

In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device (PSD) sensors and the visible light emitted from the illumination of the room in which it is located. The orientation estimation will require that the PSD sensor receives signal from either 2 or 4 light sources simultaneously. As will be shown in the article, the error determining the rotation angle of the agent with the on-board sensor is less than 0.2 degrees for two emitters. On the other hand, by using 4 light sources the three Euler rotation angles are determined, with mean errors in the measurements smaller than 0.35◦ for the x- and y-axis and 0.16◦ for the z-axis. The accuracy of the measurement has been evaluated experimentally in a 2.5 m-high ceiling room over an area of 2.2 m2 using geodetic measurement tools to establish the reference ground truth values.Junta de Comunidades de Castilla-La Manch

Dynamic Adjustment of Measurement Noise Covariance Matrix in an Infrared-based Positioning and Tracking System

2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), 5-8 September, 2022, Beijing, China.The accuracy of optical positioning systems can be compromised by multiple factors (reflections, calibration, etc.). As an alternative to the triangulation, multilateration, or fingerprinting techniques typically used in these systems, stochastic estimation techniques can be used, such as Kalman Filters (KF) in its different variants. They estimate the receiver position based on the acquired measurements and the estimated positions in previous iterations. This work presents the evaluation of a 3D optical positioning system, based on four LED beacons and a quadrant receiver, using an Extended Kalman Filter (EKF). The implementation of a measurement noise covariance matrix that is adjusted depending on the angle and distance between transmitters and receiver, obtained in the previous iteration, is analysed. The receiver position estimation using both a dynamic and a static measurement noise covariance matrix is evaluated and compared with simulations and experimental tests. In simulations, the achieved errors are below 6 cm and 12 cm in 75% of the cases when using a dynamic and a static noise matrix, respectively. In the experimental tests, the obtained errors in 75% of the cases for the position in plane XY and the rotation angle ? are smaller than 7.44 cm and 1.06 ? with a dynamic noise matrix; and below 7.59 cm and 1.62 ? for a static one.Agencia Estatal de InvestigaciónUniversidad de Alcal

Accuracy and Precision Assessment of AoA-Based Indoor Positioning Systems Using InfrastructureLighting and a Position-Sensitive Detector

Unlike GNSS-based outdoor positioning, there is no technological alternative for Indoor Positioning Systems (IPSs) that generally stands out from the others. In indoor contexts, the measurement technologies and localization strategies to be used depend strongly on the application requirements and are complementary to each other. In this work, we present an optical IPS based on a Position-Sensitive Detector (PSD) and exploiting illumination infrastructure to determine the target position by Angle of Arrival (AoA) measurements. We combine the proposed IPS with different positioning strategies depending on the number of visible emitters (one, two, or more) and available prior or additional information about the scenario and target. The accuracy and precision of the proposal is assessed experimentally for the different strategies in a 2.47 m high space covering approximately 2.2 m2, using high-end geodetic equipment to establish the reference ground truth. When the orientation of the target is known from external measurements, an average positioning error of 8.2 mm is obtained using the signal received from only one emitter. Using simultaneous observations from two emitters, an average positioning error of 9.4 mm is obtained without external information when the target movement is restricted to a plane. Conversely, if four signals are available, an average positioning error of 4.9 cm is demonstrated, yielding the complete 3D pose of the target free of any prior assumption or additional measurements. In all cases, a precision (2s) better than 5.9 mm is achieved across the complete test space for an integration time of 10 ms. The proposed system represents a prospectively useful alternative for indoor positioning applications requiring fast and reliable cm-level accuracy with moderate cost when smart illumination infrastructure is available in the environment

Indoor localization utilizing existing infrastructure in smart homes : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer and Electronics Engineering, Massey University, Albany, New Zealand

Listed in 2019 Dean's List of Exceptional ThesesIndoor positioning system (IPS) have received significant interest from the research community over the past decade. However, this has not eventuated into widespread adoption of IPS and few commercial solutions exist. Integration into Smart Homes could allow for secondary services including location-based services, targeted user experiences and intrusion detection, to be enabled using the existing underlying infrastructure. Since New Zealand has an aging population, we must ensure that the elderly are well looked after. An IPS solution could detect whether a person has been immobile for an extended period and alert medical personnel. A major shortcoming of existing IPS is their reliance on end-users to undertake a significant infrastructure investment to facilitate the localization tasks. An IPS that does not require extensive installation and calibration procedures, could potentially see significant uptake from end users. In order to expedite the widespread adoption of IPS technology, this thesis focuses on four major areas of improvement, namely: infrastructure reuse, reduced node density, algorithm improvement and reduced end user calibration requirements. The work presented demonstrates the feasibility of utilizing existing wireless and lighting infrastructure for positioning and implements novel spring-relaxation and potential fields-based localization approaches that allow for robust target tracking, with minimal calibration requirements. The developed novel localization algorithms are benchmarked against the existing state of the art and show superior performance

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Autonomous navigation and mapping of mobile robots based on 2D/3D cameras combination

Aufgrund der tendenziell zunehmenden Nachfrage an Systemen zur Unterstützung des alltäglichen Lebens gibt es derzeit ein großes Interesse an autonomen Systemen. Autonome Systeme werden in Häusern, Büros, Museen sowie in Fabriken eingesetzt. Sie können verschiedene Aufgaben erledigen, beispielsweise beim Reinigen, als Helfer im Haushalt, im Bereich der Sicherheit und Bildung, im Supermarkt sowie im Empfang als Auskunft, weil sie dazu verwendet werden können, die Verarbeitungszeit zu kontrollieren und präzise, zuverlässige Ergebnisse zu liefern. Ein Forschungsgebiet autonomer Systeme ist die Navigation und Kartenerstellung. Das heißt, mobile Roboter sollen selbständig ihre Aufgaben erledigen und zugleich eine Karte der Umgebung erstellen, um navigieren zu können. Das Hauptproblem besteht darin, dass der mobile Roboter in einer unbekannten Umgebung, in der keine zusätzlichen Bezugsinformationen vorhanden sind, das Gelände erkunden und eine dreidimensionale Karte davon erstellen muss. Der Roboter muss seine Positionen innerhalb der Karte bestimmen. Es ist notwendig, ein unterscheidbares Objekt zu finden. Daher spielen die ausgewählten Sensoren und der Register-Algorithmus eine relevante Rolle. Die Sensoren, die sowohl Tiefen- als auch Bilddaten liefern können, sind noch unzureichend. Der neue 3D-Sensor, nämlich der "Photonic Mixer Device" (PMD), erzeugt mit hoher Bildwiederholfrequenz eine Echtzeitvolumenerfassung des umliegenden Szenarios und liefert Tiefen- und Graustufendaten. Allerdings erfordert die höhere Qualität der dreidimensionalen Erkundung der Umgebung Details und Strukturen der Oberflächen, die man nur mit einer hochauflösenden CCD-Kamera erhalten kann. Die vorliegende Arbeit präsentiert somit eine Exploration eines mobilen Roboters mit Hilfe der Kombination einer CCD- und PMD-Kamera, um eine dreidimensionale Karte der Umgebung zu erstellen. Außerdem wird ein Hochleistungsalgorithmus zur Erstellung von 3D Karten und zur Poseschätzung in Echtzeit unter Verwendung des "Simultaneous Localization and Mapping" (SLAM) Verfahrens präsentiert. Der autonom arbeitende, mobile Roboter soll ferner Aufgaben übernehmen, wie z.B. die Erkennung von Objekten in ihrer Umgebung, um verschiedene praktische Aufgaben zu lösen. Die visuellen Daten der CCD-Kamera liefern nicht nur eine hohe Auflösung der Textur-Daten für die Tiefendaten, sondern werden auch für die Objekterkennung verwendet. Der "Iterative Closest Point" (ICP) Algorithmus benutzt zwei Punktwolken, um den Bewegungsvektor zu bestimmen. Schließlich sind die Auswertung der Korrespondenzen und die Rekonstruktion der Karte, um die reale Umgebung abzubilden, in dieser Arbeit enthalten.Presently, intelligent autonomous systems have to perform very interesting tasks due to trendy increases in support demands of human living. Autonomous systems have been used in various applications like houses, offices, museums as well as in factories. They are able to operate in several kinds of applications such as cleaning, household assistance, transportation, security, education and shop assistance because they can be used to control the processing time, and to provide precise and reliable output. One research field of autonomous systems is mobile robot navigation and map generation. That means the mobile robot should work autonomously while generating a map, which the robot follows. The main issue is that the mobile robot has to explore an unknown environment and to generate a three dimensional map of an unknown environment in case that there is not any further reference information. The mobile robot has to estimate its position and pose. It is required to find distinguishable objects. Therefore, the selected sensors and registered algorithms are significant. The sensors, which can provide both, depth as well as image data are still deficient. A new 3D sensor, namely the Photonic Mixer Device (PMD), generates a high rate output in real-time capturing the surrounding scenario as well as the depth and gray scale data. However, a higher quality of three dimension explorations requires details and textures of surfaces, which can be obtained from a high resolution CCD camera. This work hence presents the mobile robot exploration using the integration of CCD and PMD camera in order to create a three dimensional map. In addition, a high performance algorithm for 3D mapping and pose estimation of the locomotion in real time, using the "Simultaneous Localization and Mapping" (SLAM) technique is proposed. The flawlessly mobile robot should also handle the tasks, such as the recognition of objects in its environment, in order to achieve various practical missions. Visual input from the CCD camera not only delivers high resolution texture data on depth volume, but is also used for object recognition. The “Iterative Closest Point” (ICP) algorithm is using two sets of points to find out the translation and rotation vector between two scans. Finally, the evaluation of the correspondences and the reconstruction of the map to resemble the real environment are included in this thesis

Machine Learning Techniques for Device-Free Indoor Person Tracking

L'abstract è presente nell'allegato / the abstract is in the attachmen

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings