758 research outputs found
Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving
Autonomy, robotics, and Artificial Intelligence (AI) are among the main defining themes of next-generation societies. Of the most important applications of said technologies is driving automation which spans from different Advanced Driver Assistance Systems (ADAS) to full self-driving vehicles. Driving automation is promising to reduce accidents, increase safety, and increase access to mobility for more people such as the elderly and the handicapped. However, one of the main challenges facing autonomous vehicles is robust perception which can enable safe interaction and decision making. With so many sensors to perceive the environment, each with its own capabilities and limitations, vision is by far one of the main sensing modalities. Cameras are cheap and can provide rich information of the observed scene. Therefore, this dissertation develops a set of visual perception algorithms with a focus on autonomous driving as the target application area. This dissertation starts by addressing the problem of real-time motion estimation of an agent using only the visual input from a camera attached to it, a problem known as visual odometry. The visual odometry algorithm can achieve low drift rates over long-traveled distances. This is made possible through the innovative local mapping approach used. This visual odometry algorithm was then combined with my multi-object detection and tracking system. The tracking system operates in a tracking-by-detection paradigm where an object detector based on convolution neural networks (CNNs) is used. Therefore, the combined system can detect and track other traffic participants both in image domain and in 3D world frame while simultaneously estimating vehicle motion. This is a necessary requirement for obstacle avoidance and safe navigation. Finally, the operational range of traditional monocular cameras was expanded with the capability to infer depth and thus replace stereo and RGB-D cameras. This is accomplished through a single-stream convolution neural network which can output both depth prediction and semantic segmentation. Semantic segmentation is the process of classifying each pixel in an image and is an important step toward scene understanding. Literature survey, algorithms descriptions, and comprehensive evaluations on real-world datasets are presented.Ph.D.College of Engineering & Computer ScienceUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/153989/1/Mohamed Aladem Final Dissertation.pdfDescription of Mohamed Aladem Final Dissertation.pdf : Dissertatio
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
We present an end-to-end joint training framework that explicitly models
6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular
camera setup without supervision. Our technical contributions are three-fold.
First, we highlight the fundamental difference between inverse and forward
projection while modeling the individual motion of each rigid object, and
propose a geometrically correct projection pipeline using a neural forward
projection module. Second, we design a unified instance-aware photometric and
geometric consistency loss that holistically imposes self-supervisory signals
for every background and object region. Lastly, we introduce a general-purpose
auto-annotation scheme using any off-the-shelf instance segmentation and
optical flow models to produce video instance segmentation maps that will be
utilized as input to our training pipeline. These proposed elements are
validated in a detailed ablation study. Through extensive experiments conducted
on the KITTI and Cityscapes dataset, our framework is shown to outperform the
state-of-the-art depth and motion estimation methods. Our code, dataset, and
models are available at https://github.com/SeokjuLee/Insta-DM .Comment: Accepted to AAAI 2021. Code/dataset/models are available at
https://github.com/SeokjuLee/Insta-DM. arXiv admin note: substantial text
overlap with arXiv:1912.0935
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles
Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin
Deep Learning for Object Recognition in picking tasks
Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Curs acadèmic 2016-2017In the light of current advancement in deep learning, robot vision is not an exception. Many popular
machine learning algorithms has already been proposed and implemented to solve intricate computer
vision problems. The same has not been in the case of robot vision. Due to real time constraints and
dynamic nature of environment such as illumination and processing power, very few algorithms are
able to solve the object recognition problem at large.
The primary objective of the thesis project is to converge into an accurate working algorithm for
object recognition in a cluttered scene and subsequently helping the BAXTER robot to pick up
the correct object among the clutter. Feature matching algorithms usually fail to identify most of
the object having no texture, hence deep learning has been employed for better performance. The
next step is to look for the object and localize it within the image frame. Although basic shallow
Convolutional Neural Network easily identifies the presence of an object within a frame, it is very
difficult to localize the object location within the frame. This work primarily focuses on finding
a solution for an accurate localization. The first solution which comes to mind is to produce a
bounding box surrounding the object. In literature, YOLO is found to be providing a very robust
result on existing datasets. But this was not the case when it was tried on new objects belonging
to the current thesis project work. Due to high inaccuracy and presence of a huge redundant area
within the bounding box, an algorithm was needed which will segment the object accurately and
make the picking task easier. This was done through semantic segmentation using deep CNNs.
Although time consuming, RESNET has been found to be very efficient as its post processed output
helps to identify items in a significantly difficult task environment. This work has been done in light
of upcoming AMAZON robotic challenge where the robot successfully classified and distinguished
everyday items from a cluttered scenario. In addition to this, a performance analysis study has also
been done comparing YOLO and RESNET justifying the usage of the later algorithm with the help
of performance metrics such IOU and ViG
Traffic Scene Perception for Automated Driving with Top-View Grid Maps
Ein automatisiertes Fahrzeug muss sichere, sinnvolle und schnelle Entscheidungen auf Basis seiner Umgebung treffen.
Dies benötigt ein genaues und recheneffizientes Modell der Verkehrsumgebung.
Mit diesem Umfeldmodell sollen Messungen verschiedener Sensoren fusioniert, gefiltert und nachfolgenden Teilsysteme als kompakte, aber aussagekräftige Information bereitgestellt werden.
Diese Arbeit befasst sich mit der Modellierung der Verkehrsszene auf Basis von Top-View Grid Maps.
Im Vergleich zu anderen Umfeldmodellen ermöglichen sie eine frühe Fusion von Distanzmessungen aus verschiedenen Quellen mit geringem Rechenaufwand sowie eine explizite Modellierung von Freiraum.
Nach der Vorstellung eines Verfahrens zur Bodenoberflächenschätzung, das die Grundlage der Top-View Modellierung darstellt, werden Methoden zur Belegungs- und Elevationskartierung für Grid Maps auf Basis von mehreren, verrauschten, teilweise widersprüchlichen oder fehlenden Distanzmessungen behandelt.
Auf der resultierenden, sensorunabhängigen Repräsentation werden anschließend Modelle zur Detektion von Verkehrsteilnehmern sowie zur Schätzung von Szenenfluss, Odometrie und Tracking-Merkmalen untersucht.
Untersuchungen auf öffentlich verfügbaren Datensätzen und einem Realfahrzeug zeigen, dass Top-View Grid Maps durch on-board LiDAR Sensorik geschätzt und verlässlich sicherheitskritische Umgebungsinformationen wie Beobachtbarkeit und Befahrbarkeit abgeleitet werden können.
Schließlich werden Verkehrsteilnehmer als orientierte Bounding Boxen mit semantischen Klassen, Geschwindigkeiten und Tracking-Merkmalen aus einem gemeinsamen Modell zur Objektdetektion und Flussschätzung auf Basis der Top-View Grid Maps bestimmt
Semantic Segmentation and Completion of 2D and 3D Scenes
Semantic segmentation is one of the fundamental problems in computer vision. This thesis addresses various tasks, all related to the fine-grained, i.e. pixel-wise or voxel-wise, semantic understanding of a scene. In the recent years semantic segmentation by 2D convolutional neural networks has become as much as a default pre-processing step for many other computer vision tasks, since it outputs very rich spatially resolved feature maps and semantic labels that are useful for many higher level recognition tasks. In this thesis, we make several contributions to the field of semantic scene understanding using an image or a depth measurement, recorded by different types of laser sensors, as input. Firstly, we propose a new approach to 2D semantic segmentation of images. It consists of an adaptation of an existing approach for real time capability under constrained hardware demands that are required by a real life drone. The approach is based on a highly optimized implementation of random forests combined with a label propagation strategy. Next, we shift our focus to what we believe is one of the important next forefronts in computer vision: To give machines the ability to anticipate and extrapolate beyond what is captured in a single frame by a camera or depth sensor. This anticipation capability is what allows humans to efficiently interact with their environment. The need for this ability is most prominently displayed in the behaviour of today's autonomous cars. One of their shortcomings is that they only interpret the current sensor state, which prevents them from anticipating events which would require an adaptation of their driving policy. The result is a lot of sudden breaks and non-human-like driving behaviour, which can provoke accidents or negatively impact the traffic flow. Therefore we first propose a task to spatially anticipate semantic labels outside the field of view of an image. The task is based on the Cityscapes dataset, where each image has been center cropped. The goal is to train an algorithm that predicts the semantic segmentation map in the area outside the cropped input region. Along with the task itself, we propose an efficient iterative approach based on 2D convolutional neural networks by designing a task adapted loss function. Afterwards, we switch to the 3D domain. In three dimensions the goal shifts from assigning pixel-wise labels towards the reconstruction of the full 3D scene using a grid of labeled voxels. Thereby one has to anticipate the semantics and geometry in the space that is occluded by the objects themselves from the viewpoint of an image or laser sensor. The task is known as 3D semantic scene completion and has recently caught a lot of attention. Here we propose two new approaches that advance the performance of existing 3D semantic scene completion baselines. The first one is a two stream approach where we leverage a multi-modal input consisting of images and Kinect depth measurements in an early fusion scheme. Moreover we propose a more memory efficient input embedding. The second approach to semantic scene completion leverages the power of the recently introduced generative adversarial networks (GANs). Here we construct a network architecture that follows the GAN principles and uses a discriminator network as an additional regularizer in the 3D-CNN training. With our proposed approaches in semantic scene completion we achieve a new state-of-the-art performance on two benchmark datasets. Finally we observe that one of the shortcomings in semantic scene completion is the lack of a realistic, large scale dataset. We therefore introduce the first real world dataset for semantic scene completion based on the KITTI odometry benchmark. By semantically annotating alls scans of a 10 Hz Velodyne laser scanner, driving through urban and countryside areas, we obtain data that is valuable for many tasks including semantic scene completion. Along with the data we explore the performance of current semantic scene completion models as well as models for semantic point cloud segmentation and motion segmentation. The results show that there is still a lot of space for improvement for either tasks so our dataset is a valuable contribution for future research into these directions
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
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