Road Tracking from High resolution IRS And IKONOS Images Using Unscented Kalman Filtering

A typical way to update map is to compare recent satellite images with existing map data, detect new roads and add them as cartographic entities to the road layer. At present image processing and pattern recognition are not robust enough to automate the image interpretation system feasible. For this reason we have to develop an image interpretation system that rely on human guidance. More importantly road maps require final checking by a human due to the legal implementations of error. Our proposed technique is applied to IRS and IKONOS images using Unscented Kalman Filter(UKF) . UKF is used for tracing the median axis of the single road segment. The Extended Kalman Filter (EKF) is probably the most widely used estimation algorithm for road tracking. However, more than 35 years of experience in the estimation community has shown that is difficult to implement and is difficult to tune. To overcome this limitation,UKF is introduced in road tracking which is more accurate, easier to implement, and uses the same order of calculations as linearization. The principles and algorithm of EKF and UKF were also discussed. The core of our system is based on profile matching.UKF traces the roadbeyond obstacles and tries to find the continuation of the road finding all road branches initializing at the road junction.The completeness and correctness of road tracking from the IRS and IKONOS images were also compared

Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art

In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Robot for Robotour 2012

Cílem práce je teoretická analýza, návrh a implementace metody pro provoz autonomního robota s pomocí kamery a senzorů, jenž by umožňoval účast v soutěži Robotour 2012. Klíčové problémy jsou: počítačové vidění, nejistota robotické snímání a lokalizační problém. Za prvé, extrakce cesty je dosažena pomoci metody segmentace barev. Za druhé, pro kombinaci informací různých nejistých zdrojů se navrhuje Kalmanův filtr. Konečně, snímky získané z kamery slouží k budování mapy lokální mřížky obsazenosti, která je korigovaná pomocí sensorů a je de facto prezentace prostředí pro robota.The aim of this article is the theoretical analysis, proposal and implementation of a method for the operation of an autonomous robot for Robotour 2012 with the help of camera and sensor usage. Key issues to be faced are the computer vision, robot sensing uncertainty and the localization problem. Firstly, road extraction is achieved by using colour segmentation. Secondly, for the combination of information of different uncertain sources, Kalman Filter is proposed. Finally, the information received by the camera which serves for the building of an occupancy grid map corrected by sensors is de facto the representation of environment for the robot.

Improvement of detection and tracking techniques in multistatic passive radar systems. (Mejora de técnicas de detección y seguimiento en sistemas radar pasivos multiestáticos)

Esta tesis doctoral es el resultado de una intensa actividad investigadora centrada en los sensores radar pasivos para la mejora de las capacidades de detección y seguimiento en escenarios complejos con blancos terrestres y pequeños drones. El trabajo de investigación se ha llevado a cabo en el grupo de investigación coordinado por la Dra. María Pilar Jarabo Amores, dentro del marco diferentes proyectos: IDEPAR (“Improved DEtection techniques for PAssive Radars”), MASTERSAT (“MultichAnnel paSsive radar receiver exploiting TERrestrial and SATellite Illuminators”) y KRIPTON (“A Knowledge based appRoach to passIve radar detection using wideband sPace adapTive prOcessiNg”) financiados por el Ministerio de Economía y Competitividad de España; MAPIS (Multichannel passive ISAR imaging for military applications) y JAMPAR (“JAMmer-based PAssive Radar”), financiados por la Agencia Europea de Defensa (EDA) . El objetivo principal es la mejora de las técnicas de detección y seguimiento en radares pasivos con configuraciones biestáticas y multiestaticas. En el documento se desarrollan algoritmos para el aprovechamiento de señales procedentes de distintos iluminadores de oportunidad (transmisores DVB-T, satélites DVB-S y señales GPS). Las soluciones propuestas han sido integradas en el demostrador tecnológico IDEPAR, desarrollado y actualizado bajo los proyectos mencionados, y validadas en escenarios reales declarados de interés por potenciales usuarios finales (Direccion general de armamento y material, instituto nacional de tecnología aeroespacial y la armada española). Para el desarrollo y evaluación de cadenas de las cadenas de procesado, se plantean dos casos de estudio: blancos terrestres en escenarios semiurbanos edificios y pequeños blancos aéreos en escenarios rurales y costeros. Las principales contribuciones se pueden resumir en los siguientes puntos: • Diseño de técnicas de seguimiento 2D en el espacio de trabajo rango biestático-frecuencia Doppler: se desarrollan técnicas de seguimiento para los dos casos de estudio, localización de blancos terrestres y pequeños drones. Para es último se implementan técnicas capaces de seguir tanto el movimiento del dron como su firma Doppler, lo que permite implementar técnicas de clasificación de blancos. • Diseño de técnicas de seguimiento de blancos capaces de integrar información en el espacio 3D (rango, Doppler y acimut): se diseñan técnicas basadas en procesado en dos etapas, una primera con seguimiento en 2D para el filtrado de falsas alarmas y la segunda para el seguimiento en 3D y la conversión de coordenadas a un plano local cartesiano. Se comparan soluciones basadas en filtros de Kalman para sistemas tanto lineales como no lineales. • Diseño de cadenas de procesado para sistemas multiestáticos: la información estimada del blanco sobre múltiples geometrías biestáticas es utilizada para incremento de las capacidades de localización del blanco en el plano cartesiano local. Se presentan soluciones basadas en filtros de Kalman para sistemas no lineales explotando diferentes medidas biestáticas en el proceso de transformación de coordenadas, analizando las mejoras de precisión en la localización del blanco. • Diseño de etapas de procesado para radares pasivos basados en señales satelitales de las constelaciones GPS DVB-S. Se estudian las características de las señales satelitales identificando sus inconvenientes y proponiendo cadenas de procesado que permitan su utilización para la detección y seguimiento de blancos terrestres. • Estudio del uso de señales DVB-T multicanal con gaps de transmisión entre los diferentes canales en sistemas radares pasivos. Con ello se incrementa la resolución del sistema, y las capacidades de detección, seguimiento y localización. Se estudia el modelo de señal multicanal, sus efectos sobre el procesado coherente y se proponen cadenas de procesado para paliar los efectos adversos de este tipo de señales

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored

Vehicle Tracking and Motion Estimation Based on Stereo Vision Sequences

In this dissertation, a novel approach for estimating trajectories of road vehicles such as cars, vans, or motorbikes, based on stereo image sequences is presented. Moving objects are detected and reliably tracked in real-time from within a moving car. The resulting information on the pose and motion state of other moving objects with respect to the own vehicle is an essential basis for future driver assistance and safety systems, e.g., for collision prediction. The focus of this contribution is on oncoming traffic, while most existing work in the literature addresses tracking the lead vehicle. The overall approach is generic and scalable to a variety of traffic scenes including inner city, country road, and highway scenarios. A considerable part of this thesis addresses oncoming traffic at urban intersections. The parameters to be estimated include the 3D position and orientation of an object relative to the ego-vehicle, as well as the object's shape, dimension, velocity, acceleration and the rotational velocity (yaw rate). The key idea is to derive these parameters from a set of tracked 3D points on the object's surface, which are registered to a time-consistent object coordinate system, by means of an extended Kalman filter. Combining the rigid 3D point cloud model with the dynamic model of a vehicle is one main contribution of this thesis. Vehicle tracking at intersections requires covering a wide range of different object dynamics, since vehicles can turn quickly. Three different approaches for tracking objects during highly dynamic turn maneuvers up to extreme maneuvers such as skidding are presented and compared. These approaches allow for an online adaptation of the filter parameter values, overcoming manual parameter tuning depending on the dynamics of the tracked object in the scene. This is the second main contribution. Further issues include the introduction of two initialization methods, a robust outlier handling, a probabilistic approach for assigning new points to a tracked object, as well as mid-level fusion of the vision-based approach with a radar sensor. The overall system is systematically evaluated both on simulated and real-world data. The experimental results show the proposed system is able to accurately estimate the object pose and motion parameters in a variety of challenging situations, including night scenes, quick turn maneuvers, and partial occlusions. The limits of the system are also carefully investigated.In dieser Dissertation wird ein Ansatz zur Trajektorienschätzung von Straßenfahrzeugen (PKW, Lieferwagen, Motorräder,...) anhand von Stereo-Bildfolgen vorgestellt. Bewegte Objekte werden in Echtzeit aus einem fahrenden Auto heraus automatisch detektiert, vermessen und deren Bewegungszustand relativ zum eigenen Fahrzeug zuverlässig bestimmt. Die gewonnenen Informationen liefern einen entscheidenden Grundstein für zukünftige Fahrerassistenz- und Sicherheitssysteme im Automobilbereich, beispielsweise zur Kollisionsprädiktion. Während der Großteil der existierenden Literatur das Detektieren und Verfolgen vorausfahrender Fahrzeuge in Autobahnszenarien adressiert, setzt diese Arbeit einen Schwerpunkt auf den Gegenverkehr, speziell an städtischen Kreuzungen. Der Ansatz ist jedoch grundsätzlich generisch und skalierbar für eine Vielzahl an Verkehrssituationen (Innenstadt, Landstraße, Autobahn). Die zu schätzenden Parameter beinhalten die räumliche Lage des anderen Fahrzeugs relativ zum eigenen Fahrzeug, die Objekt-Geschwindigkeit und -Längsbeschleunigung, sowie die Rotationsgeschwindigkeit (Gierrate) des beobachteten Objektes. Zusätzlich werden die Objektabmaße sowie die Objektform rekonstruiert. Die Grundidee ist es, diese Parameter anhand der Transformation von beobachteten 3D Punkten, welche eine ortsfeste Position auf der Objektoberfläche besitzen, mittels eines rekursiven Schätzers (Kalman Filter) zu bestimmen. Ein wesentlicher Beitrag dieser Arbeit liegt in der Kombination des Starrkörpermodells der Punktewolke mit einem Fahrzeugbewegungsmodell. An Kreuzungen können sehr unterschiedliche Dynamiken auftreten, von einer Geradeausfahrt mit konstanter Geschwindigkeit bis hin zum raschen Abbiegen. Um eine manuelle Parameteradaption abhängig von der jeweiligen Szene zu vermeiden, werden drei verschiedene Ansätze zur automatisierten Anpassung der Filterparameter an die vorliegende Situation vorgestellt und verglichen. Dies stellt den zweiten Hauptbeitrag der Arbeit dar. Weitere wichtige Beiträge sind zwei alternative Initialisierungsmethoden, eine robuste Ausreißerbehandlung, ein probabilistischer Ansatz zur Zuordnung neuer Objektpunkte, sowie die Fusion des bildbasierten Verfahrens mit einem Radar-Sensor. Das Gesamtsystem wird im Rahmen dieser Arbeit systematisch anhand von simulierten und realen Straßenverkehrsszenen evaluiert. Die Ergebnisse zeigen, dass das vorgestellte Verfahren in der Lage ist, die unbekannten Objektparameter auch unter schwierigen Umgebungsbedingungen, beispielsweise bei Nacht, schnellen Abbiegemanövern oder unter Teilverdeckungen, sehr präzise zu schätzen. Die Grenzen des Systems werden ebenfalls sorgfältig untersucht