41 research outputs found
Visible Light Positioning and Navigation with Noise Mitigation Using Allan Variance
Visible Light Positioning (VLP) has become an essential candidate for high-accurate positioning; however, its positioning accuracy is usually degraded by the noise in the VLP system. To solve this problem, a novel scheme of noise measurement and mitigation is proposed for VLPbased on the noise measurement from Allan Varianceand the noise mitigation from positioning algorithms such asAdaptive Least Squares (ALSQ)andExtended Kalman Filter (EKF). In this scheme, Allan Varianceis introduced for noise analysis in VLPfor the first time, which provides an efficient method for measuring the white noise in the VLPsystems. Meanwhile, we evaluate our noise reduction method under static testusing ALSQ and dynamic test using EKF. Furthermore, this article carefully discusses the relationship between positioning accuracy and Dilution of Precision (DOP) values. The preliminary field static tests demonstrate that the proposed scheme improves thepositioning accuracy by 16.5% and achieves the accuracy of 137mmwhile dynamic tests show an improvement of 60.4% and achieve the mean positioning accuracyof 153 mm
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
Localization and Mapping for Autonomous Driving: Fault Detection and Reliability Analysis
Autonomous driving has advanced rapidly during the past decades and has expanded its application for multiple fields, both indoor and outdoor. One of the significant issues associated with a highly automated vehicle (HAV) is how to increase the safety level. A key requirement to ensure the safety of automated driving is the ability of reliable localization and navigation, with which intelligent vehicle/robot systems could successfully make reliable decisions for the driving path or react to the sudden events occurring within the path. A map with rich environment information is essential to support autonomous driving system to meet these high requirements. Therefore, multi-sensor-based localization and mapping methods are studied in this Thesis.
Although some studies have been conducted in this area, a full quality control scheme to guarantee the reliability and to detect outliers in localization and mapping systems is still lacking. The quality of the integration system has not been sufficiently evaluated. In this research, an extended Kalman filter and smoother based quality control (EKF/KS QC) scheme is investigated and has been successfully applied for different localization and mapping scenarios. An EKF/KS QC toolbox is developed in MATLAB, which can be easily embedded and applied into different localization and mapping scenarios. The major contributions of this research are:
a) The equivalence between least squares and smoothing is discussed, and an extended Kalman filter-smoother quality control method is developed according to this equivalence, which can not only be used to deal with system model outlier with detection, and identification, can also be used to analyse, control and improve the system quality. Relevant mathematical models of this quality control method have been developed to deal with issues such as singular measurement covariance matrices, and numerical instability of smoothing.
b) Quality control analysis is conducted for different positioning system, including Global Navigation Satellite System (GNSS) multi constellation integration for both Real Time Kinematic (RTK) and Post Processing Kinematic (PPK), and the integration of GNSS and Inertial Navigation System (INS). The results indicate PPK method can provide more reliable positioning results than RTK. With the proposed quality control method, the influence of the detected outlier can be mitigated by directly correcting the input measurement with the estimated outlier value, or by adapting the final estimation results with the estimated outlier’s influence value.
c) Mathematical modelling and quality control aspects for online simultaneous localization and mapping (SLAM) are examined. A smoother based offline SLAM method is investigated with quality control. Both outdoor and indoor datasets have been tested with these SLAM methods. Geometry analysis for the SLAM system has been done according to the quality control results. The system reliability analysis is essential for the SLAM designer as it can be conducted at the early stage without real-world measurement.
d) A least squares based localization method is proposed that treats the High-Definition (HD) map as a sensor source. This map-based sensor information is integrated with other perception sensors, which significantly improves localization efficiency and accuracy. Geometry analysis is undertaken with the quality measures to analyse the influence of the geometry upon the estimation solution and the system quality, which can be hints for future design of the localization system.
e) A GNSS/INS aided LiDAR mapping and localization procedure is developed. A high-density map is generated offline, then, LiDAR-based localization can be undertaken online with this pre-generated map. Quality control is conducted for this system. The results demonstrate that the LiDAR based localization within map can effectively improve the accuracy and reliability compared to the GNSS/INS only system, especially during the period that GNSS signal is lost
Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations
Zandian R. Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations. Bielefeld: Universität Bielefeld; 2019.This thesis discusses the utilization of ultra-wideband (UWB) technology in indoor localization scenarios and proposes system setup and evaluates different localization algorithms in order to improve the localization accuracy and stability of such systems in non-ideal conditions of the indoor environment.
Recent developments and advances of technology in the areas of ubiquitous Internet, robotics and internet of things (IoT) have resulted in emerging new application areas in daily life in which localization systems are vital. The significant demand for a robust and accurate localization system that is applicable in indoor areas lacking satellites link, can be sensed. The UWB technology offers accurate localization systems with an accuracy of below 10 cm and covering the range of up to a few hundred meters thanks to their dedicated large bandwidth, modulation technique and signal power.
In this thesis, the technology behind the UWB systems is discussed in detail. In terms of localization topologies, different scenarios with the focus on time-based methods are introduced. The main focus of this thesis is on the differential time of arrival localization systems (TDoA) with unilateral constellation that is suitable for robotic localization and navigation applications.
A new approach for synchronization of TDoA topology is proposed and influence of clock inaccuracies in such systems are thoroughly evaluated. For localization engine, two groups of static and dynamic iterative algorithms are introduced. Among the possible dynamic methods, extended Kalman filter (EKF), H∞ and unscented Kalman filter (UKF) are discussed and meticulously evaluated.
In order to tackle the non-line of sight (NLOS) problem of such systems, for detection stage several solutions which are based on parametric machine learning methods are proposed. Furthermore, for mitigation phase two solutions namely adjustment of measurement variance and innovation term are suggested. Practical results prove the efficiency and high reliability of the proposed algorithms with positive NLOS condition detection rate of more than 87%.
In practical trials, the localization system is evaluated in indoor and outdoor arenas in both line of sight and non-line of sight conditions. The results show that the proposed detection and mitigation methods can be successfully applied for both small and large-scale arenas with the higher performance of the localization filters in terms of accuracy in large-scale scenarios
Sistemas de posicionamento baseados em comunicação por luz para ambientes interiores
The demand for highly precise indoor positioning systems (IPSs) is growing
rapidly due to its potential in the increasingly popular techniques of the
Internet of Things, smart mobile devices, and artificial intelligence. IPS
becomes a promising research domain that is getting wide attention due to its
benefits in several working scenarios, such as, industries, indoor public
locations, and autonomous navigation. Moreover, IPS has a prominent
contribution in day-to-day activities in organizations such as health care
centers, airports, shopping malls, manufacturing, underground locations, etc.,
for safe operating environments. In indoor environments, both radio frequency
(RF) and optical wireless communication (OWC) based technologies could be
adopted for localization. Although the RF-based global positioning system,
such as, Global positioning system offers higher penetration rates with
reduced accuracy (i.e., in the range of a few meters), it does not work well in
indoor environments (and not at all in certain cases such as tunnels, mines,
etc.) due to the very weak signal and no direct access to the satellites. On the
other hand, the light-based system known as a visible light positioning (VLP)
system, as part of the OWC systems, uses the pre-existing light-emitting
diodes (LEDs)-based lighting infrastructure, could be used at low cost and
high accuracy compared with the RF-based systems. VLP is an emerging
technology promising high accuracy, high security, low deployment cost,
shorter time response, and low relative complexity when compared with RFbased
positioning.
However, in indoor VLP systems, there are some concerns such as,
multipath reflection, transmitter tilting, transmitter’s position, and orientation
uncertainty, human shadowing/blocking, and noise causing the increase in
the positioning error, thereby reducing the positioning accuracy of the system.
Therefore, it is imperative to capture the characteristics of different VLP
channel and properly model them for the dual purpose of illumination and
localization. In this thesis, firstly, the impact of transmitter tilting angles and
multipath reflections are studied and for the first time, it is demonstrated that
tilting the transmitter can be beneficial in VLP systems considering both line of
sight (LOS) and non-line of sight transmission paths. With the transmitters
oriented towards the center of the receiving plane, the received power level is
maximized due to the LOS components. It is also shown that the proposed
scheme offers a significant accuracy improvement of up to ~66% compared
with a typical non-tilted transmitter VLP. The effect of tilting the transmitter on
the lighting uniformity is also investigated and results proved that the
uniformity achieved complies with the European Standard EN 12464-1.
After that, the impact of transmitter position and orientation uncertainty on
the accuracy of the VLP system based on the received signal strength (RSS)
is investigated. Simulation results show that the transmitter uncertainties have
a severe impact on the positioning error, which can be leveraged through the
usage of more transmitters. Concerning a smaller transmitter’s position
epochs, and the size of the training set. It is shown that,
the ANN with Bayesian regularization outperforms the traditional RSS
technique using the non-linear least square estimation for all values of signal
to noise ratio.
Furthermore, a novel indoor VLP system is proposed based on support
vector machines and polynomial regression considering two different
multipath environments of an empty room and a furnished room. The results
show that, in an empty room, the positioning accuracy improvement for the
positioning error of 2.5 cm are 36.1, 58.3, and 72.2 % for three different
scenarios according to the regions’ distribution in the room. For the furnished
room, a positioning relative accuracy improvement of 214, 170, and 100 % is
observed for positioning error of 0.1, 0.2, and 0.3 m, respectively. Ultimately,
an indoor VLP system based on convolutional neural networks (CNN) is
proposed and demonstrated experimentally in which LEDs are used as
transmitters and a rolling shutter camera is used as receiver. A detection
algorithm named single shot detector (SSD) is used which relies on CNN (i.e.,
MobileNet or ResNet) for classification as well as position estimation of each
LED in the image. The system is validated using a real-world size test setup
containing eight LED luminaries. The obtained results show that the maximum
average root mean square positioning error achieved is 4.67 and 5.27 cm with
SSD MobileNet and SSD ResNet models, respectively. The validation results
show that the system can process 67 images per second, allowing real-time
positioning.A procura por sistemas de posicionamento interior (IPSs) de alta precisão tem
crescido rapidamente devido ao seu interesse nas técnicas cada vez mais
populares da Internet das Coisas, dispositivos móveis inteligentes e
inteligência artificial. O IPS tornou-se um domínio de pesquisa promissor que
tem atraído grande atenção devido aos seus benefícios em vários cenários de
trabalho, como indústrias, locais públicos e navegação autónoma. Além disso,
o IPS tem uma contribuição destacada no dia a dia de organizações, como,
centros de saúde, aeroportos, supermercados, fábricas, locais subterrâneos,
etc. As tecnologias baseadas em radiofrequência (RF) e comunicação óptica
sem fio (OWC) podem ser adotadas para localização em ambientes interiores.
Embora o sistema de posicionamento global (GPS) baseado em RF ofereça
taxas de penetração mais altas com precisão reduzida (ou seja, na faixa de
alguns metros), não funciona bem em ambientes interiores (e não funciona
bem em certos casos como túneis, minas, etc.) devido ao sinal muito fraco e
falta de acesso direto aos satélites. Por outro lado, o sistema baseado em luz
conhecido como sistema de posicionamento de luz visível (VLP), como parte
dos sistemas OWC, usa a infraestrutura de iluminação baseada em díodos
emissores de luz (LEDs) pré-existentes, é um sistemas de baixo custo e alta
precisão quando comprado com os sistemas baseados em RF. O VLP é uma
tecnologia emergente que promete alta precisão, alta segurança, baixo custo
de implantação, menor tempo de resposta e baixa complexidade relativa
quando comparado ao posicionamento baseado em RF.
No entanto, os sistemas VLP interiores, exibem algumas limitações, como, a
reflexão multicaminho, inclinação do transmissor, posição do transmissor e
incerteza de orientação, sombra/bloqueio humano e ruído, que têm como
consequência o aumento do erro de posicionamento, e consequente redução
da precisão do sistema. Portanto, é imperativo estudar as características dos
diferentes canais VLP e modelá-los adequadamente para o duplo propósito de
iluminação e localização. Esta tesa aborda, primeiramente, o impacto dos
ângulos de inclinação do transmissor e reflexões multipercurso no
desempenho do sistema de posicionamento. Demonstra-se que a inclinação
do transmissor pode ser benéfica em sistemas VLP considerando tanto a linha
de vista (LOS) como as reflexões. Com os transmissores orientados para o
centro do plano recetor, o nível de potência recebido é maximizado devido aos
componentes LOS. Também é mostrado que o esquema proposto oferece
uma melhoria significativa de precisão de até ~66% em comparação com um
sistema VLP de transmissor não inclinado típico. O efeito da inclinação do
transmissor na uniformidade da iluminação também é investigado e os
resultados comprovam que a uniformidade alcançada está de acordo com a
Norma Europeia EN 12464-1.
O impacto da posição do transmissor e incerteza de orientação na precisão
do sistema VLP com base na intensidade do sinal recebido (RSS) foi também investigado. Os resultados da simulação mostram que as incertezas do
transmissor têm um impacto severo no erro de posicionamento, que pode ser
atenuado com o uso de mais transmissores. Para incertezas de
posicionamento dos transmissores menores que 5 cm, os erros médios de
posicionamento são 23.3, 15.1 e 13.2 cm para conjuntos de 4, 9 e 16
transmissores, respetivamente. Enquanto que, para a incerteza de orientação
de um transmissor menor de 5°, os erros médios de posicionamento são 31.9,
20.6 e 17 cm para conjuntos de 4, 9 e 16 transmissores, respetivamente.
O trabalho da tese abordou a investigação dos aspetos de projeto de um
sistema VLP indoor no qual uma rede neuronal artificial (ANN) é utilizada para
estimativa de posicionamento considerando um canal multipercurso. O estudo
considerou a influência do ruído como indicador de desempenho para a
comparação entre diferentes abordagens de projeto. Três algoritmos de treino
de ANNs diferentes foram considerados, a saber, Levenberg-Marquardt,
regularização Bayesiana e algoritmos de gradiente conjugado escalonado,
para minimizar o erro de posicionamento no sistema VLP. O projeto da ANN foi
otimizado com base no número de neurónios nas camadas ocultas, no número
de épocas de treino e no tamanho do conjunto de treino. Mostrou-se que, a
ANN com regularização Bayesiana superou a técnica RSS tradicional usando
a estimação não linear dos mínimos quadrados para todos os valores da
relação sinal-ruído.
Foi proposto um novo sistema VLP indoor baseado em máquinas de vetores
de suporte (SVM) e regressão polinomial considerando dois ambientes
interiores diferentes: uma sala vazia e uma sala mobiliada. Os resultados
mostraram que, numa sala vazia, a melhoria da precisão de posicionamento
para o erro de posicionamento de 2.5 cm são 36.1, 58.3 e 72.2% para três
cenários diferentes de acordo com a distribuição das regiões na sala. Para a
sala mobiliada, uma melhoria de precisão relativa de posicionamento de 214,
170 e 100% é observada para erro de posicionamento de 0.1, 0.2 e 0.3 m,
respetivamente.
Finalmente, foi proposto um sistema VLP indoor baseado em redes neurais
convolucionais (CNN). O sistema foi demonstrado experimentalmente usando
luminárias LED como transmissores e uma camara com obturador rotativo
como recetor. O algoritmo de detecção usou um detector de disparo único
(SSD) baseado numa CNN pré configurada (ou seja, MobileNet ou ResNet)
para classificação. O sistema foi validado usando uma configuração de teste
de tamanho real contendo oito luminárias LED. Os resultados obtidos
mostraram que o erro de posicionamento quadrático médio alcançado é de
4.67 e 5.27 cm com os modelos SSD MobileNet e SSD ResNet,
respetivamente. Os resultados da validação mostram que o sistema pode
processar 67 imagens por segundo, permitindo o posicionamento em tempo
real.Programa Doutoral em Engenharia Eletrotécnic
Smart Monitoring and Control in the Future Internet of Things
The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things
Intelligent sensing for robot mapping and simultaneous human localization and activity recognition
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Ph. D.) -- Bilkent University, 2011.Includes bibliographical references leaves 147-163.We consider three different problems in two different sensing domains, namely
ultrasonic sensing and inertial sensing. Since the applications considered in each
domain are inherently different, this thesis is composed of two main parts. The
approach common to the two parts is that raw data acquired from simple sensors
is processed intelligently to extract useful information about the environment.
In the first part, we employ active snake contours and Kohonen’s selforganizing
feature maps (SOMs) for representing and evaluating discrete point
maps of indoor environments efficiently and compactly. We develop a generic
error criterion for comparing two different sets of points based on the Euclidean
distance measure. The point sets can be chosen as (i) two different sets of map
points acquired with different mapping techniques or different sensing modalities,
(ii) two sets of fitted curve points to maps extracted by different mapping techniques
or sensing modalities, or (iii) a set of extracted map points and a set of
fitted curve points. The error criterion makes it possible to compare the accuracy
of maps obtained with different techniques among themselves, as well as with an
absolute reference. We optimize the parameters of active snake contours and
SOMs using uniform sampling of the parameter space and particle swarm optimization.
A demonstrative example from ultrasonic mapping is given based on
experimental data and compared with a very accurate laser map, considered an
absolute reference. Both techniques can fill the erroneous gaps in discrete point
maps. Snake curve fitting results in more accurate maps than SOMs because it is
more robust to outliers. The two methods and the error criterion are sufficiently
general that they can also be applied to discrete point maps acquired with other
mapping techniques and other sensing modalities.
In the second part, we use body-worn inertial/magnetic sensor units for recognition
of daily and sports activities, as well as for human localization in GPSdenied
environments. Each sensor unit comprises a tri-axial gyroscope, a tri-axial
accelerometer, and a tri-axial magnetometer. The error characteristics of the sensors
are modeled using the Allan variance technique, and the parameters of lowand
high-frequency error components are estimated.
Then, we provide a comparative study on the different techniques of classifying
human activities that are performed using body-worn miniature inertial and
magnetic sensors. Human activities are classified using five sensor units worn
on the chest, the arms, and the legs. We compute a large number of features
extracted from the sensor data, and reduce these features using both Principal
Components Analysis (PCA) and sequential forward feature selection (SFFS).
We consider eight different pattern recognition techniques and provide a comparison
in terms of the correct classification rates, computational costs, and their
training and storage requirements. Results with sensors mounted on various locations
on the body are also provided. The results indicate that if the system
is trained by the data of an individual person, it is possible to obtain over 99%
correct classification rates with a simple quadratic classifier such as the Bayesian
decision method. However, if the training data of that person are not available
beforehand, one has to resort to more complex classifiers with an expected correct
classification rate of about 85%.
We also consider the human localization problem using body-worn inertial/
magnetic sensors. Inertial sensors are characterized by drift error caused
by the integration of their rate output to get position information. Because of
this drift, the position and orientation data obtained from inertial sensor signals
are reliable over only short periods of time. Therefore, position updates from externally
referenced sensors are essential. However, if the map of the environment
is known, the activity context of the user provides information about position. In
particular, the switches in the activity context correspond to discrete locations
on the map. By performing activity recognition simultaneously with localization,
one can detect the activity context switches and use the corresponding position
information as position updates in the localization filter. The localization filter
also involves a smoother, which combines the two estimates obtained by running
the zero-velocity update (ZUPT) algorithm both forward and backward in time.
We performed experiments with eight subjects in an indoor and an outdoor environment
involving “walking,” “turning,” and “standing” activities. Using the
error criterion in the first part of the thesis, we show that the position errors can
be decreased by about 85% on the average. We also present the results of a 3-D
experiment performed in a realistic indoor environment and demonstrate that it
is possible to achieve over 90% error reduction in position by performing activity
recognition simultaneously with localization.Altun, KeremPh.D
Contemporary Robotics
This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials
Sensing and Signal Processing in Smart Healthcare
In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included