2,583 research outputs found
A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics
We survey the current state of millimeterwave (mmWave) radar applications in
robotics with a focus on unique capabilities, and discuss future opportunities
based on the state of the art. Frequency Modulated Continuous Wave (FMCW)
mmWave radars operating in the 76--81GHz range are an appealing alternative to
lidars, cameras and other sensors operating in the near visual spectrum. Radar
has been made more widely available in new packaging classes, more convenient
for robotics and its longer wavelengths have the ability to bypass visual
clutter such as fog, dust, and smoke. We begin by covering radar principles as
they relate to robotics. We then review the relevant new research across a
broad spectrum of robotics applications beginning with motion estimation,
localization, and mapping. We then cover object detection and classification,
and then close with an analysis of current datasets and calibration techniques
that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin
A Joint 3D-2D based Method for Free Space Detection on Roads
In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object detection. Typical 3D point clouds do not have enough
resolution to detect fine differences in heights such as between road and
pavement. Image based 2D cues fail when encountering uneven road textures such
as due to shadows, potholes, lane markings or road restoration. We propose a
novel free road space detection technique combining both 2D and 3D cues. In
particular, we use CNN based road segmentation from 2D images and plane/box
fitting on sparse depth data obtained from SLAM as priors to formulate an
energy minimization using conditional random field (CRF), for road pixels
classification. While the CNN learns the road texture and is unaffected by
depth boundaries, the 3D information helps in overcoming texture based
classification failures. Finally, we use the obtained road segmentation with
the 3D depth data from monocular SLAM to detect the free space for the
navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset,
as well as videos captured by us, validate the superiority of the proposed
approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201
LiDAR-Based Object Tracking and Shape Estimation
Umfeldwahrnehmung stellt eine Grundvoraussetzung für den sicheren und komfortablen Betrieb automatisierter Fahrzeuge dar. Insbesondere bewegte Verkehrsteilnehmer in der unmittelbaren Fahrzeugumgebung haben dabei große Auswirkungen auf die Wahl einer angemessenen Fahrstrategie. Dies macht ein System zur Objektwahrnehmung notwendig, welches eine robuste und präzise Zustandsschätzung der Fremdfahrzeugbewegung und -geometrie zur Verfügung stellt.
Im Kontext des automatisierten Fahrens hat sich das Box-Geometriemodell über die Zeit als Quasistandard durchgesetzt. Allerdings stellt die Box aufgrund der ständig steigenden Anforderungen an Wahrnehmungssysteme inzwischen häufig eine unerwünscht grobe Approximation der tatsächlichen Geometrie anderer Verkehrsteilnehmer dar. Dies motiviert einen Übergang zu genaueren Formrepräsentationen.
In der vorliegenden Arbeit wird daher ein probabilistisches Verfahren zur gleichzeitigen Schätzung von starrer Objektform und -bewegung mittels Messdaten eines LiDAR-Sensors vorgestellt. Der Vergleich dreier Freiform-Geometriemodelle mit verschiedenen Detaillierungsgraden (Polygonzug, Dreiecksnetz und Surfel Map) gegenüber dem einfachen Boxmodell zeigt, dass die Reduktion von Modellierungsfehlern in der Objektgeometrie eine robustere und präzisere Parameterschätzung von Objektzuständen ermöglicht. Darüber hinaus können automatisierte Fahrfunktionen, wie beispielsweise ein Park- oder Ausweichassistent, von einem genaueren Wissen über die Fremdobjektform profitieren.
Es existieren zwei Einflussgrößen, welche die Auswahl einer angemessenen Formrepräsentation maßgeblich beeinflussen sollten: Beobachtbarkeit (Welchen Detaillierungsgrad lässt die Sensorspezifikation theoretisch zu?) und Modell-Adäquatheit (Wie gut bildet das gegebene Modell die tatsächlichen Beobachtungen ab?). Auf Basis dieser Einflussgrößen wird in der vorliegenden Arbeit eine Strategie zur Modellauswahl vorgestellt, die zur Laufzeit adaptiv das am besten geeignete Formmodell bestimmt.
Während die Mehrzahl der Algorithmen zur LiDAR-basierten Objektverfolgung ausschließlich auf Punktmessungen zurückgreift, werden in der vorliegenden Arbeit zwei weitere Arten von Messungen vorgeschlagen: Information über den vermessenen Freiraum wird verwendet, um über Bereiche zu schlussfolgern, welche nicht von Objektgeometrie belegt sein können. Des Weiteren werden LiDAR-Intensitäten einbezogen, um markante Merkmale wie Nummernschilder und Retroreflektoren zu detektieren und über die Zeit zu verfolgen.
Eine ausführliche Auswertung auf über 1,5 Stunden von aufgezeichneten Fremdfahrzeugtrajektorien im urbanen Bereich und auf der Autobahn zeigen, dass eine präzise Modellierung der Objektoberfläche die Bewegungsschätzung um bis zu 30%-40% verbessern kann. Darüber hinaus wird gezeigt, dass die vorgestellten Methoden konsistente und hochpräzise Rekonstruktionen von Objektgeometrien generieren können, welche die häufig signifikante Überapproximation durch das einfache Boxmodell vermeiden
Percepção do ambiente urbano e navegação usando visão robótica : concepção e implementação aplicado à veículo autônomo
Orientadores: Janito Vaqueiro Ferreira, Alessandro Corrêa VictorinoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O desenvolvimento de veículos autônomos capazes de se locomover em ruas urbanas pode proporcionar importantes benefícios na redução de acidentes, no aumentando da qualidade de vida e também na redução de custos. Veículos inteligentes, por exemplo, frequentemente baseiam suas decisões em observações obtidas a partir de vários sensores tais como LIDAR, GPS e câmeras. Atualmente, sensores de câmera têm recebido grande atenção pelo motivo de que eles são de baixo custo, fáceis de utilizar e fornecem dados com rica informação. Ambientes urbanos representam um interessante mas também desafiador cenário neste contexto, onde o traçado das ruas podem ser muito complexos, a presença de objetos tais como árvores, bicicletas, veículos podem gerar observações parciais e também estas observações são muitas vezes ruidosas ou ainda perdidas devido a completas oclusões. Portanto, o processo de percepção por natureza precisa ser capaz de lidar com a incerteza no conhecimento do mundo em torno do veículo. Nesta tese, este problema de percepção é analisado para a condução nos ambientes urbanos associado com a capacidade de realizar um deslocamento seguro baseado no processo de tomada de decisão em navegação autônoma. Projeta-se um sistema de percepção que permita veículos robóticos a trafegar autonomamente nas ruas, sem a necessidade de adaptar a infraestrutura, sem o conhecimento prévio do ambiente e considerando a presença de objetos dinâmicos tais como veículos. Propõe-se um novo método baseado em aprendizado de máquina para extrair o contexto semântico usando um par de imagens estéreo, a qual é vinculada a uma grade de ocupação evidencial que modela as incertezas de um ambiente urbano desconhecido, aplicando a teoria de Dempster-Shafer. Para a tomada de decisão no planejamento do caminho, aplica-se a abordagem dos tentáculos virtuais para gerar possíveis caminhos a partir do centro de referencia do veículo e com base nisto, duas novas estratégias são propostas. Em primeiro, uma nova estratégia para escolher o caminho correto para melhor evitar obstáculos e seguir a tarefa local no contexto da navegação hibrida e, em segundo, um novo controle de malha fechada baseado na odometria visual e o tentáculo virtual é modelado para execução do seguimento de caminho. Finalmente, um completo sistema automotivo integrando os modelos de percepção, planejamento e controle são implementados e validados experimentalmente em condições reais usando um veículo autônomo experimental, onde os resultados mostram que a abordagem desenvolvida realiza com sucesso uma segura navegação local com base em sensores de câmeraAbstract: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context, where the road layout may be very complex, the presence of objects such as trees, bicycles, cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to deal with uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully, understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement based on decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, without the need to adapt the infrastructure, without requiring previous knowledge of the environment and considering the presence of dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and to follow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensorsDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic
Sensormodelle zur Simulation der Umfelderfassung für Systeme des automatisierten Fahrens
The use of sensor models allows the simulation of environmental perception in automated driving systems, aiding in development and testing efforts. This work systematically discusses the different types of sensor models and introduces an architecture for statistics based as well as for physically motivated sensor models. Each approach is grounded in real world observations of sensor measurements and is designed for portability and the ease of further extensions.Die Nutzung von Sensormodellen für die Umfelderfassung ebnet den Weg für die simulationsgestützte Entwicklung von Systemen des automatisierten Fahrens. In dieser Arbeit wird eine Systematik für verschiedene Arten von Sensormodellen eingeführt und eine Umsetzung von statistischen sowie von physikalisch motivierten Modellen vorgestellt. Beide Ansätze basieren auf realen Sensormessdaten und zielen auf eine leichte Übertragbarkeit sowie die Möglichkeit der Erweiterung der Modelle für verschiedene Anwendungsbereiche
Multimodal perception for autonomous driving
Mención Internacional en el título de doctorAutonomous driving is set to play an important role among intelligent
transportation systems in the coming decades. The advantages
of its large-scale implementation –reduced accidents, shorter commuting
times, or higher fuel efficiency– have made its development a priority
for academia and industry. However, there is still a long way to
go to achieve full self-driving vehicles, capable of dealing with any
scenario without human intervention. To this end, advances in control,
navigation and, especially, environment perception technologies
are yet required. In particular, the detection of other road users that
may interfere with the vehicle’s trajectory is a key element, since it
allows to model the current traffic situation and, thus, to make decisions
accordingly.
The objective of this thesis is to provide solutions to some of
the main challenges of on-board perception systems, such as extrinsic
calibration of sensors, object detection, and deployment on
real platforms. First, a calibration method for obtaining the relative
transformation between pairs of sensors is introduced, eliminating
the complex manual adjustment of these parameters. The algorithm
makes use of an original calibration pattern and supports LiDARs,
and monocular and stereo cameras. Second, different deep learning
models for 3D object detection using LiDAR data in its bird’s eye
view projection are presented. Through a novel encoding, the use
of architectures tailored to image detection is proposed to process
the 3D information of point clouds in real time. Furthermore, the
effectiveness of using this projection together with image features is
analyzed. Finally, a method to mitigate the accuracy drop of LiDARbased
detection networks when deployed in ad-hoc configurations is
introduced. For this purpose, the simulation of virtual signals mimicking
the specifications of the desired real device is used to generate
new annotated datasets that can be used to train the models.
The performance of the proposed methods is evaluated against
other existing alternatives using reference benchmarks in the field of
computer vision (KITTI and nuScenes) and through experiments in
open traffic with an automated vehicle. The results obtained demonstrate
the relevance of the presented work and its suitability for commercial
use.La conducción autónoma está llamada a jugar un papel importante en
los sistemas inteligentes de transporte de las próximas décadas. Las
ventajas de su implementación a larga escala –disminución de accidentes,
reducción del tiempo de trayecto, u optimización del consumo–
han convertido su desarrollo en una prioridad para la academia y
la industria. Sin embargo, todavía hay un largo camino por delante
hasta alcanzar una automatización total, capaz de enfrentarse a cualquier
escenario sin intervención humana. Para ello, aún se requieren
avances en las tecnologías de control, navegación y, especialmente,
percepción del entorno. Concretamente, la detección de otros usuarios
de la carretera que puedan interferir en la trayectoria del vehículo
es una pieza fundamental para conseguirlo, puesto que permite modelar
el estado actual del tráfico y tomar decisiones en consecuencia.
El objetivo de esta tesis es aportar soluciones a algunos de los
principales retos de los sistemas de percepción embarcados, como
la calibración extrínseca de los sensores, la detección de objetos, y su
despliegue en plataformas reales. En primer lugar, se introduce un
método para la obtención de la transformación relativa entre pares
de sensores, eliminando el complejo ajuste manual de estos parámetros.
El algoritmo hace uso de un patrón de calibración propio y da
soporte a cámaras monoculares, estéreo, y LiDAR. En segundo lugar,
se presentan diferentes modelos de aprendizaje profundo para la detección
de objectos en 3D utilizando datos de escáneres LiDAR en su
proyección en vista de pájaro. A través de una nueva codificación, se
propone la utilización de arquitecturas de detección en imagen para
procesar en tiempo real la información tridimensional de las nubes
de puntos. Además, se analiza la efectividad del uso de esta proyección
junto con características procedentes de imágenes. Por último,
se introduce un método para mitigar la pérdida de precisión de las
redes de detección basadas en LiDAR cuando son desplegadas en
configuraciones ad-hoc. Para ello, se plantea la simulación de señales
virtuales con las características del modelo real que se quiere utilizar,
generando así nuevos conjuntos anotados para entrenar los modelos.
El rendimiento de los métodos propuestos es evaluado frente a
otras alternativas existentes haciendo uso de bases de datos de referencia
en el campo de la visión por computador (KITTI y nuScenes),
y mediante experimentos en tráfico abierto empleando un vehículo
automatizado. Los resultados obtenidos demuestran la relevancia de
los trabajos presentados y su viabilidad para un uso comercial.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Jesús García Herrero.- Secretario: Ignacio Parra Alonso.- Vocal: Gustavo Adolfo Peláez Coronad
Optical measurement of shape and deformation fields on challenging surfaces
A multiple-sensor optical shape measurement system (SMS) based on the principle of white-light fringe projection has been developed and commercialised by Loughborough University and Phase Vision Ltd for over 10 years. The use of the temporal phase unwrapping technique allows precise and dense shape measurements of complex surfaces; and the photogrammetry-based calibration technique offers the ability to calibrate multiple sensors simultaneously in order to achieve 360° measurement coverage. Nevertheless, to enhance the applicability of the SMS in industrial environments, further developments are needed (i) to improve the calibration speed for quicker deployment, (ii) to broaden the application range from shape measurement to deformation field measurement, and (iii) to tackle practically-challenging surfaces of which specular components may disrupt the acquired data and result in spurious measurements. The calibration process typically requires manual positioning of an artefact (i.e., reference object) at many locations within the view of the sensors. This is not only timeconsuming but also complicated for an operator with average knowledge of metrology. This thesis introduces an automated artefact positioning system which enables automatic and optimised distribution of the artefacts, automatic prediction of their whereabouts to increase the artefact detection speed and robustness, and thereby greater overall calibration performance. This thesis also describes a novel technique that integrates the digital image correlation (DIC) technique into the present fringe projection SMS for the purpose of simultaneous shape and deformation field measurement. This combined technique offers three key advantages: (a) the ability to deal with geometrical discontinuities which are commonly present on mechanical surfaces and currently challenging to most deformation measurement methods, (b) the ability to measure 3D displacement fields with a basic single-camera single-projector SMS with no additional hardware components, and (c) the simple implementation on a multiple-sensor hardware platform to achieve complete coverage of large-scale and complex samples, with the resulting displacement fields automatically lying in a single global coordinate system. A displacement measurement iii accuracy of ≅1/12,000 of the measurement volume, which is comparable to that of an industry-standard DIC system, has been achieved. The applications of this novel technique to several structural tests of aircraft wing panels on-site at the research centre of Airbus UK in Filton are also presented. Mechanical components with shiny surface finish and complex geometry may introduce another challenge to present fringe projection techniques. In certain circumstances, multiple reflections of the projected fringes on an object surface may cause ambiguity in the phase estimation process and result in incorrect coordinate measurements. This thesis presents a new technique which adopts a Fourier domain ranging (FDR) method to correctly identifying multiple phase signals and enables unambiguous triangulation for a measured coordinate. Experiments of the new FDR technique on various types of surfaces have shown promising results as compared to the traditional phase unwrapping techniques
Incremental map refinement of building information using lidar point clouds
For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty - even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System
Sensormodelle zur Simulation der Umfelderfassung für Systeme des automatisierten Fahrens
The use of sensor models allows the simulation of environmental perception in automated driving systems, aiding in development and testing efforts. This work systematically discusses the different types of sensor models and introduces an architecture for statistics based as well as for physically motivated sensor models. Each approach is grounded in real world observations of sensor measurements and is designed for portability and the ease of further extensions.Die Nutzung von Sensormodellen für die Umfelderfassung ebnet den Weg für die simulationsgestützte Entwicklung von Systemen des automatisierten Fahrens. In dieser Arbeit wird eine Systematik für verschiedene Arten von Sensormodellen eingeführt und eine Umsetzung von statistischen sowie von physikalisch motivierten Modellen vorgestellt. Beide Ansätze basieren auf realen Sensormessdaten und zielen auf eine leichte Übertragbarkeit sowie die Möglichkeit der Erweiterung der Modelle für verschiedene Anwendungsbereiche
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