Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook

peer reviewedWhen fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-Accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Using Radio Frequency and Motion Sensing to Improve Camera Sensor Systems

Camera-based sensor systems have advanced significantly in recent years. This advancement is a combination of camera CMOS (complementary metal-oxide-semiconductor) hardware technology improvement and new computer vision (CV) algorithms that can better process the rich information captured. As the world becoming more connected and digitized through increased deployment of various sensors, cameras have become a cost-effective solution with the advantages of small sensor size, intuitive sensing results, rich visual information, and neural network-friendly. The increased deployment and advantages of camera-based sensor systems have fueled applications such as surveillance, object detection, person re-identification, scene reconstruction, visual tracking, pose estimation, and localization. However, camera-based sensor systems have fundamental limitations such as extreme power consumption, privacy-intrusive, and inability to see-through obstacles and other non-ideal visual conditions such as darkness, smoke, and fog. In this dissertation, we aim to improve the capability and performance of camera-based sensor systems by utilizing additional sensing modalities such as commodity WiFi and mmWave (millimeter wave) radios, and ultra-low-power and low-cost sensors such as inertial measurement units (IMU). In particular, we set out to study three problems: (1) power and storage consumption of continuous-vision wearable cameras, (2) human presence detection, localization, and re-identification in both indoor and outdoor spaces, and (3) augmenting the sensing capability of camera-based systems in non-ideal situations. We propose to use an ultra-low-power, low-cost IMU sensor, along with readily available camera information, to solve the first problem. WiFi devices will be utilized in the second problem, where our goal is to reduce the hardware deployment cost and leverage existing WiFi infrastructure as much as possible. Finally, we will use a low-cost, off-the-shelf mmWave radar to extend the sensing capability of a camera in non-ideal visual sensing situations.Doctor of Philosoph

From motion capture to interactive virtual worlds : towards unconstrained motion-capture algorithms for real-time performance-driven character animation

This dissertation takes performance-driven character animation as a representative application and advances motion capture algorithms and animation methods to meet its high demands. Existing approaches have either coarse resolution and restricted capture volume, require expensive and complex multi-camera systems, or use intrusive suits and controllers. For motion capture, set-up time is reduced using fewer cameras, accuracy is increased despite occlusions and general environments, initialization is automated, and free roaming is enabled by egocentric cameras. For animation, increased robustness enables the use of low-cost sensors input, custom control gesture definition is guided to support novice users, and animation expressiveness is increased. The important contributions are: 1) an analytic and differentiable visibility model for pose optimization under strong occlusions, 2) a volumetric contour model for automatic actor initialization in general scenes, 3) a method to annotate and augment image-pose databases automatically, 4) the utilization of unlabeled examples for character control, and 5) the generalization and disambiguation of cyclical gestures for faithful character animation. In summary, the whole process of human motion capture, processing, and application to animation is advanced. These advances on the state of the art have the potential to improve many interactive applications, within and outside virtual reality.Diese Arbeit befasst sich mit Performance-driven Character Animation, insbesondere werden Motion Capture-Algorithmen entwickelt um den hohen Anforderungen dieser Beispielanwendung gerecht zu werden. Existierende Methoden haben entweder eine geringe Genauigkeit und einen eingeschränkten Aufnahmebereich oder benötigen teure Multi-Kamera-Systeme, oder benutzen störende Controller und spezielle Anzüge. Für Motion Capture wird die Setup-Zeit verkürzt, die Genauigkeit für Verdeckungen und generelle Umgebungen erhöht, die Initialisierung automatisiert, und Bewegungseinschränkung verringert. Für Character Animation wird die Robustheit für ungenaue Sensoren erhöht, Hilfe für benutzerdefinierte Gestendefinition geboten, und die Ausdrucksstärke der Animation verbessert. Die wichtigsten Beiträge sind: 1) ein analytisches und differenzierbares Sichtbarkeitsmodell für Rekonstruktionen unter starken Verdeckungen, 2) ein volumetrisches Konturenmodell für automatische Körpermodellinitialisierung in genereller Umgebung, 3) eine Methode zur automatischen Annotation von Posen und Augmentation von Bildern in großen Datenbanken, 4) das Nutzen von Beispielbewegungen für Character Animation, und 5) die Generalisierung und Übertragung von zyklischen Gesten für genaue Charakteranimation. Es wird der gesamte Prozess erweitert, von Motion Capture bis hin zu Charakteranimation. Die Verbesserungen sind für viele interaktive Anwendungen geeignet, innerhalb und außerhalb von virtueller Realität

Listening to Distances and Hearing Shapes:Inverse Problems in Room Acoustics and Beyond

A central theme of this thesis is using echoes to achieve useful, interesting, and sometimes surprising results. One should have no doubts about the echoes' constructive potential; it is, after all, demonstrated masterfully by Nature. Just think about the bat's intriguing ability to navigate in unknown spaces and hunt for insects by listening to echoes of its calls, or about similar (albeit less well-known) abilities of toothed whales, some birds, shrews, and ultimately people. We show that, perhaps contrary to conventional wisdom, multipath propagation resulting from echoes is our friend. When we think about it the right way, it reveals essential geometric information about the sources--channel--receivers system. The key idea is to think of echoes as being more than just delayed and attenuated peaks in 1D impulse responses; they are actually additional sources with their corresponding 3D locations. This transformation allows us to forget about the abstract \emph{room}, and to replace it by more familiar \emph{point sets}. We can then engage the powerful machinery of Euclidean distance geometry. A problem that always arises is that we do not know \emph{a priori} the matching between the peaks and the points in space, and solving the inverse problem is achieved by \emph{echo sorting}---a tool we developed for learning correct labelings of echoes. This has applications beyond acoustics, whenever one deals with waves and reflections, or more generally, time-of-flight measurements. Equipped with this perspective, we first address the ``Can one hear the shape of a room?'' question, and we answer it with a qualified ``yes''. Even a single impulse response uniquely describes a convex polyhedral room, whereas a more practical algorithm to reconstruct the room's geometry uses only first-order echoes and a few microphones. Next, we show how different problems of localization benefit from echoes. The first one is multiple indoor sound source localization. Assuming the room is known, we show that discretizing the Helmholtz equation yields a system of sparse reconstruction problems linked by the common sparsity pattern. By exploiting the full bandwidth of the sources, we show that it is possible to localize multiple unknown sound sources using only a single microphone. We then look at indoor localization with known pulses from the geometric echo perspective introduced previously. Echo sorting enables localization in non-convex rooms without a line-of-sight path, and localization with a single omni-directional sensor, which is impossible without echoes. A closely related problem is microphone position calibration; we show that echoes can help even without assuming that the room is known. Using echoes, we can localize arbitrary numbers of microphones at unknown locations in an unknown room using only one source at an unknown location---for example a finger snap---and get the room's geometry as a byproduct. Our study of source localization outgrew the initial form factor when we looked at source localization with spherical microphone arrays. Spherical signals appear well beyond spherical microphone arrays; for example, any signal defined on Earth's surface lives on a sphere. This resulted in the first slight departure from the main theme: We develop the theory and algorithms for sampling sparse signals on the sphere using finite rate-of-innovation principles and apply it to various signal processing problems on the sphere

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

From motion capture to interactive virtual worlds : towards unconstrained motion-capture algorithms for real-time performance-driven character animation

This dissertation takes performance-driven character animation as a representative application and advances motion capture algorithms and animation methods to meet its high demands. Existing approaches have either coarse resolution and restricted capture volume, require expensive and complex multi-camera systems, or use intrusive suits and controllers. For motion capture, set-up time is reduced using fewer cameras, accuracy is increased despite occlusions and general environments, initialization is automated, and free roaming is enabled by egocentric cameras. For animation, increased robustness enables the use of low-cost sensors input, custom control gesture definition is guided to support novice users, and animation expressiveness is increased. The important contributions are: 1) an analytic and differentiable visibility model for pose optimization under strong occlusions, 2) a volumetric contour model for automatic actor initialization in general scenes, 3) a method to annotate and augment image-pose databases automatically, 4) the utilization of unlabeled examples for character control, and 5) the generalization and disambiguation of cyclical gestures for faithful character animation. In summary, the whole process of human motion capture, processing, and application to animation is advanced. These advances on the state of the art have the potential to improve many interactive applications, within and outside virtual reality.Diese Arbeit befasst sich mit Performance-driven Character Animation, insbesondere werden Motion Capture-Algorithmen entwickelt um den hohen Anforderungen dieser Beispielanwendung gerecht zu werden. Existierende Methoden haben entweder eine geringe Genauigkeit und einen eingeschränkten Aufnahmebereich oder benötigen teure Multi-Kamera-Systeme, oder benutzen störende Controller und spezielle Anzüge. Für Motion Capture wird die Setup-Zeit verkürzt, die Genauigkeit für Verdeckungen und generelle Umgebungen erhöht, die Initialisierung automatisiert, und Bewegungseinschränkung verringert. Für Character Animation wird die Robustheit für ungenaue Sensoren erhöht, Hilfe für benutzerdefinierte Gestendefinition geboten, und die Ausdrucksstärke der Animation verbessert. Die wichtigsten Beiträge sind: 1) ein analytisches und differenzierbares Sichtbarkeitsmodell für Rekonstruktionen unter starken Verdeckungen, 2) ein volumetrisches Konturenmodell für automatische Körpermodellinitialisierung in genereller Umgebung, 3) eine Methode zur automatischen Annotation von Posen und Augmentation von Bildern in großen Datenbanken, 4) das Nutzen von Beispielbewegungen für Character Animation, und 5) die Generalisierung und Übertragung von zyklischen Gesten für genaue Charakteranimation. Es wird der gesamte Prozess erweitert, von Motion Capture bis hin zu Charakteranimation. Die Verbesserungen sind für viele interaktive Anwendungen geeignet, innerhalb und außerhalb von virtueller Realität