A Wearable RFID-Based Navigation System for the Visually Impaired

Recent studies have focused on developing advanced assistive devices to help blind or visually impaired people. Navigation is challenging for this community; however, developing a simple yet reliable navigation system is still an unmet need. This study targets the navigation problem and proposes a wearable assistive system. We developed a smart glove and shoe set based on radio-frequency identification technology to assist visually impaired people with navigation and orientation in indoor environments. The system enables the user to find the directions through audio feedback. To evaluate the device's performance, we designed a simple experimental setup. The proposed system has a simple structure and can be personalized according to the user's requirements. The results identified that the platform is reliable, power efficient, and accurate enough for indoor navigation.Comment: 6 pages, 6 figures, 3 table

Multi-UAV wireless positioning using adaptive multidimensional scaling and extended Kalman filter

Global Navigation Satellite System (GNSS) signal can be blocked when flight vehicles operate in challenging environments such as indoor or adversarial environments. While multi-UAVs are teamed during flight, cooperative localization becomes available to tackle this challenge. Multidimensional Scaling (MDS) method has been well studied for cooperative localization of Wireless Sensor Network (WSN) based on radio frequency (RF) measurement. When noise RF measurement model is lacking, conventional weighted MDS method represents confidence with the measurements by assigning weights relying on distance information between each pair of nodes. In order to process non-distance RF measurements, we present an improved weighted MDS method which applies a novel weighting scheme. In this article, the proposed method conducts velocity estimation for multi-UAV system based on odometry and Frequency Difference of Arrival (FDOA) measurements. Furthermore, an extended Kalman Filter (EKF) algorithm is applied to refine the initial estimation of the MDS method and derive position estimation. Finally, numerical experiments demonstrate the robustness and accuracy of the adaptive MDS-EKF refinement framework for multi-UAV system localization in an unknown dynamic environment lacking measurement noise information.UK Government Foreign, Commonwealth and Development Office: Chevening Scholarship. European Union funding: 778305

Velocity-Based Channel Charting with Spatial Distribution Map Matching

Fingerprint-based localization improves the positioning performance in challenging, non-line-of-sight (NLoS) dominated indoor environments. However, fingerprinting models require an expensive life-cycle management including recording and labeling of radio signals for the initial training and regularly at environmental changes. Alternatively, channel-charting avoids this labeling effort as it implicitly associates relative coordinates to the recorded radio signals. Then, with reference real-world coordinates (positions) we can use such charts for positioning tasks. However, current channel-charting approaches lag behind fingerprinting in their positioning accuracy and still require reference samples for localization, regular data recording and labeling to keep the models up to date. Hence, we propose a novel framework that does not require reference positions. We only require information from velocity information, e.g., from pedestrian dead reckoning or odometry to model the channel charts, and topological map information, e.g., a building floor plan, to transform the channel charts into real coordinates. We evaluate our approach on two different real-world datasets using 5G and distributed single-input/multiple-output system (SIMO) radio systems. Our experiments show that even with noisy velocity estimates and coarse map information, we achieve similar position accuraciesComment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Unmanned aerial vehicle positioning using 5G new radio technology in urban environment

Unmanned aerial vehicles (UAVs) are becoming increasingly popular for various applications, including surveillance, monitoring, mapping, delivery, and inspection. However, their positioning capabilities in urban environments can be limited due to challenges such as Non-Line-of-Sight (NLOS) propagation, multi-path interference, and signal blockage caused by tall buildings, trees, and other obstacles, which can affect their positioning capabilities. The purpose of this paper is to provide a novel approach for UAV’s positioning based on Observed Time Difference of Arrival (OTDOA), combining 5G (NR) technology and an inertial measurement unit (IMU) to improve UAV positioning in urban environments. Integrating these technologies can improve UAV positioning and control systems by offering rapid, low-latency communication, a thorough and precise comprehension of the UAV’s surroundings and its own condition, and more accurate assessments of the UAV’s location, speed, and orientation. Simulation model shows the data from these sensors is then fused using an Extended Kalman Filter (EKF) to estimate the UAV’s position and orientation. The study shows that the proposed system delivers accurate and reliable UAV positioning in these environments, outperforming traditional methods

Sensor Fusion for Mobile Robot Localization using UWB and ArUco Markers

Uma das principais características para considerar um robô autónomo é o facto de este ser capaz de se localizar, em tempo real, no seu ambiente, ou seja saber a sua posição e orientação. Esta é uma área desafiante que tem sido estudada por diversos investigadores em todo o mundo. Para obter a localização de um robô é possível recorrer a diferentes metodologias. No entanto há metodologias que apresentam problemas em diferentes circunstâncias, como é o caso da odometria que sofre de acumulação de erros com a distância percorrida pelo robô. Outro problema existente em diversas metodologias é a incerteza na deteção do robô devido a ruído presente nos sensores. Com o intuito de obter uma localização mais robusta do robô e mais tolerante a falhas é possível combinar diversos sistemas de localização, combinando assim as vantagens de cada um deles. Neste trabalho, será utilizado o sistema Pozyx, uma solução de baixo custo que fornece informação de posicionamento com o auxílio da tecnologia Ultra-WideBand Time-of-Flight (UWB ToF). Também serão utilizados marcadores ArUco colocados no ambiente que através da sua identificação por uma câmara é também possível obter informação de posicionamento. Estas duas soluções irão ser estudadas e implementadas num robô móvel, através de um esquema de localização baseada em marcadores. Primeiramente, irá ser feita uma caracterização do erro de ambos os sistemas, uma vez que as medidas não são perfeitas, havendo sempre algum ruído nas medições. De seguida, as medidas fornecidas pelos sistemas irão ser filtradas e fundidas com os valores da odometria do robô através da implementação de um Filtro de Kalman Extendido (EKF). Assim, é possível obter a pose do robô (posição e orientação), pose esta que é comparada com a pose fornecida por um sistema de Ground-Truth igualmente desenvolvido para este trabalho com o auxílio da libraria ArUco, percebendo assim a precisão do algoritmo desenvolvido. O trabalho desenvolvido mostrou que com a utilização do sistema Pozyx e dos marcadores ArUco é possível melhorar a localização do robô, o que significa que é uma solução adequada e eficaz para este fim.One of the main characteristics to consider a robot truly autonomous is the fact that it is able to locate itself, in real time, in its environment, that is, to know its position and orientation. This is a challenging area that has been studied by several researchers around the world. To obtain the localization of a robot it is possible to use different methodologies. However, there are methodologies that present problems in different circumstances, as is the case of odometry that suffers from error accumulation with the distance traveled by the robot. Another problem existing in several methodologies is the uncertainty in the sensing of the robot due to noise present in the sensors. In order to obtain a more robust localization of the robot and more fault tolerant it is possible to combine several localization systems, thus combining the advantages of each one. In this work, the Pozyx system will be used, a low-cost solution that provides positioning information through Ultra-WideBand Time-of-Flight (UWB ToF) technology. It will also be used ArUco markers placed in the environment that through their identification by a camera it is also possible to obtain positioning information. These two solutions will be studied and implemented in a mobile robot, through a beacon-based localization scheme. First, an error characterization of both systems will be performed, since the measurements are not perfect, and there is always some noise in the measurements. Next, the measurements provided by the systems will be filtered and fused with the robot's odometry values by the implementation of an Extended Kalman Filter (EKF). In this way, it is possible to obtain the robot's pose, i.e position and orientation, which is compared with the pose provided by a Ground-Truth system also developed for this work with the aid of the ArUco library, thus realizing the accuracy of the developed algorithm. The developed work showed that with the use of the Pozyx system and ArUco markers it is possible to improve the robot localization, meaning that it is an adequate and effective solution for this purpose