Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

A multi-hypothesis approach for range-only simultaneous localization and mapping with aerial robots

Los sistemas de Range-only SLAM (o RO-SLAM) tienen como objetivo la construcción de un mapa formado por la posición de un conjunto de sensores de distancia y la localización simultánea del robot con respecto a dicho mapa, utilizando únicamente para ello medidas de distancia. Los sensores de distancia son dispositivos capaces de medir la distancia relativa entre cada par de dispositivos. Estos sensores son especialmente interesantes para su applicación a vehículos aéreos debido a su reducido tamaño y peso. Además, estos dispositivos son capaces de operar en interiores o zonas con carencia de señal GPS y no requieren de una línea de visión directa entre cada par de dispositivos a diferencia de otros sensores como cámaras o sensores laser, permitiendo así obtener una lectura de datos continuada sin oclusiones. Sin embargo, estos sensores presentan un modelo de observación no lineal con una deficiencia de rango debido a la carencia de información de orientación relativa entre cada par de sensores. Además, cuando se incrementa la dimensionalidad del problema de 2D a 3D para su aplicación a vehículos aéreos, el número de variables ocultas del modelo aumenta haciendo el problema más costoso computacionalmente especialmente ante implementaciones multi-hipótesis. Esta tesis estudia y propone diferentes métodos que permitan la aplicación eficiente de estos sistemas RO-SLAM con vehículos terrestres o aéreos en entornos reales. Para ello se estudia la escalabilidad del sistema en relación al número de variables ocultas y el número de dispositivos a posicionar en el mapa. A diferencia de otros métodos descritos en la literatura de RO-SLAM, los algoritmos propuestos en esta tesis tienen en cuenta las correlaciones existentes entre cada par de dispositivos especialmente para la integración de medidas estÃa˛ticas entre pares de sensores del mapa. Además, esta tesis estudia el ruido y las medidas espúreas que puedan generar los sensores de distancia para mejorar la robustez de los algoritmos propuestos con técnicas de detección y filtración. También se proponen métodos de integración de medidas de otros sensores como cámaras, altímetros o GPS para refinar las estimaciones realizadas por el sistema RO-SLAM. Otros capítulos estudian y proponen técnicas para la integración de los algoritmos RO-SLAM presentados a sistemas con múltiples robots, así como el uso de técnicas de percepción activa que permitan reducir la incertidumbre del sistema ante trayectorias con carencia de trilateración entre el robot y los sensores de destancia estáticos del mapa. Todos los métodos propuestos han sido validados mediante simulaciones y experimentos con sistemas reales detallados en esta tesis. Además, todos los sistemas software implementados, así como los conjuntos de datos registrados durante la experimentación han sido publicados y documentados para su uso en la comunidad científica

Delayed-State Information Filter for Cooperative Decentralized Tracking.

Abstract-This paper presents a decentralized data fusion approach to perform cooperative perception with data gathered from heterogeneous sensors, which can be static or carried by robots. Particularly, a Decentralized Delayed-State Extended Information Filter (DDSEIF) is described, where full state trajectories are considered to fuse the information. This permits to obtain an estimation equal to that obtained by a centralized system, and allows delays and latency in the communications. The sparseness of the information matrix maintains the communications overhead at a reasonable level. The method is applied to cooperative tracking and some results in disaster management scenarios are shown. In this kind of scenarios the target might move in both open field and indoor areas, so fusion of data provided by heterogeneous sensors is beneficial

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Localisation and tracking of people using distributed UWB sensors

In vielen Überwachungs- und Rettungsszenarien ist die Lokalisierung und Verfolgung von Personen in Innenräumen auf nichtkooperative Weise erforderlich. Für die Erkennung von Objekten durch Wände in kurzer bis mittlerer Entfernung, ist die Ultrabreitband (UWB) Radartechnologie aufgrund ihrer hohen zeitlichen Auflösung und Durchdringungsfähigkeit Erfolg versprechend. In dieser Arbeit wird ein Prozess vorgestellt, mit dem Personen in Innenräumen mittels UWB-Sensoren lokalisiert werden können. Er umfasst neben der Erfassung von Messdaten, Abstandschätzungen und dem Erkennen von Mehrfachzielen auch deren Ortung und Verfolgung. Aufgrund der schwachen Reflektion von Personen im Vergleich zum Rest der Umgebung, wird zur Personenerkennung zuerst eine Hintergrundsubtraktionsmethode verwendet. Danach wird eine konstante Falschalarmrate Methode zur Detektion und Abstandschätzung von Personen angewendet. Für Mehrfachziellokalisierung mit einem UWB-Sensor wird eine Assoziationsmethode entwickelt, um die Schätzungen des Zielabstandes den richtigen Zielen zuzuordnen. In Szenarien mit mehreren Zielen kann es vorkommen, dass ein näher zum Sensor positioniertes Ziel ein anderes abschattet. Ein Konzept für ein verteiltes UWB-Sensornetzwerk wird vorgestellt, in dem sich das Sichtfeld des Systems durch die Verwendung mehrerer Sensoren mit unterschiedlichen Blickfeldern erweitert lässt. Hierbei wurde ein Prototyp entwickelt, der durch Fusion von Sensordaten die Verfolgung von Mehrfachzielen in Echtzeit ermöglicht. Dabei spielen insbesondere auch Synchronisierungs- und Kooperationsaspekte eine entscheidende Rolle. Sensordaten können durch Zeitversatz und systematische Fehler gestört sein. Falschmessungen und Rauschen in den Messungen beeinflussen die Genauigkeit der Schätzergebnisse. Weitere Erkenntnisse über die Zielzustände können durch die Nutzung zeitlicher Informationen gewonnen werden. Ein Mehrfachzielverfolgungssystem wird auf der Grundlage des Wahrscheinlichkeitshypothesenfilters (Probability Hypothesis Density Filter) entwickelt, und die Unterschiede in der Systemleistung werden bezüglich der von den Sensoren ausgegebene Informationen, d.h. die Fusion von Ortungsinformationen und die Fusion von Abstandsinformationen, untersucht. Die Information, dass ein Ziel detektiert werden sollte, wenn es aufgrund von Abschattungen durch andere Ziele im Szenario nicht erkannt wurde, wird als dynamische Überdeckungswahrscheinlichkeit beschrieben. Die dynamische Überdeckungswahrscheinlichkeit wird in das Verfolgungssystem integriert, wodurch weniger Sensoren verwendet werden können, während gleichzeitig die Performanz des Schätzers in diesem Szenario verbessert wird. Bei der Methodenauswahl und -entwicklung wurde die Anforderung einer Echtzeitanwendung bei unbekannten Szenarien berücksichtigt. Jeder untersuchte Aspekt der Mehrpersonenlokalisierung wurde im Rahmen dieser Arbeit mit Hilfe von Simulationen und Messungen in einer realistischen Umgebung mit UWB Sensoren verifiziert.Indoor localisation and tracking of people in non-cooperative manner is important in many surveillance and rescue applications. Ultra wideband (UWB) radar technology is promising for through-wall detection of objects in short to medium distances due to its high temporal resolution and penetration capability. This thesis tackles the problem of localisation of people in indoor scenarios using UWB sensors. It follows the process from measurement acquisition, multiple target detection and range estimation to multiple target localisation and tracking. Due to the weak reflection of people compared to the rest of the environment, a background subtraction method is initially used for the detection of people. Subsequently, a constant false alarm rate method is applied for detection and range estimation of multiple persons. For multiple target localisation using a single UWB sensor, an association method is developed to assign target range estimates to the correct targets. In the presence of multiple targets it can happen that targets closer to the sensor induce shadowing over the environment hindering the detection of other targets. A concept for a distributed UWB sensor network is presented aiming at extending the field of view of the system by using several sensors with different fields of view. A real-time operational prototype has been developed taking into consideration sensor cooperation and synchronisation aspects, as well as fusion of the information provided by all sensors. Sensor data may be erroneous due to sensor bias and time offset. Incorrect measurements and measurement noise influence the accuracy of the estimation results. Additional insight of the targets states can be gained by exploiting temporal information. A multiple person tracking framework is developed based on the probability hypothesis density filter, and the differences in system performance are highlighted with respect to the information provided by the sensors i.e. location information fusion vs range information fusion. The information that a target should have been detected when it is not due to shadowing induced by other targets is described as dynamic occlusion probability. The dynamic occlusion probability is incorporated into the tracking framework, allowing fewer sensors to be used while improving the tracker performance in the scenario. The method selection and development has taken into consideration real-time application requirements for unknown scenarios at every step. Each investigated aspect of multiple person localization within the scope of this thesis has been verified using simulations and measurements in a realistic environment using M-sequence UWB sensors