Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190{\deg}+ field of view covering the entire 360{\deg} around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation System

Point Cloud Processing for Environmental Analysis in Autonomous Driving using Deep Learning

Autonomous self-driving cars need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types, such as lidar scanners, are in use. This thesis contributes highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds originating from lidar sensors. First, a single-shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and joint probabilistic tracking to stabilize predictions and filter outliers. The last part presents an evaluation of data from automotive-grade lidar scanners. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation.One of the main objectives of leading automotive companies is autonomous self-driving cars. They need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types are in use. Besides cameras, lidar scanners became very important. The development in that field is significant for future applications and system integration because lidar offers a more accurate depth representation, independent from environmental illumination. Especially algorithms and machine learning approaches, including Deep Learning and Artificial Intelligence based on raw laser scanner data, are very important due to the long range and three-dimensional resolution of the measured point clouds. Consequently, a broad field of research with many challenges and unsolved tasks has been established. This thesis aims to address this deficit and contribute highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds. First, a single shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and a joint probabilistic tracking to stabilize predictions and filter outliers. In the last part, a concept for deployment into an existing test vehicle focuses on the semi-automated generation of a suitable dataset. Subsequently, an evaluation of data from automotive-grade lidar scanners is presented. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation. Experiments on the acquired application-specific and benchmark datasets show that the presented methods compete with a variety of state-of-the-art algorithms while being trimmed down to efficiency for use in self-driving cars. Furthermore, they include an extensive set of standard evaluation metrics and results to form a solid baseline for future research.Eines der Hauptziele führender Automobilhersteller sind autonome Fahrzeuge. Sie benötigen ein sehr präzises System für die Wahrnehmung der Umgebung, dass für jedes denkbare Szenario überall auf der Welt funktioniert. Daher sind verschiedene Arten von Sensoren im Einsatz, sodass neben Kameras u. a. auch Lidar Sensoren ein wichtiger Bestandteil sind. Die Entwicklung auf diesem Gebiet ist für künftige Anwendungen von höchster Bedeutung, da Lidare eine genauere, von der Umgebungsbeleuchtung unabhängige, Tiefendarstellung bieten. Insbesondere Algorithmen und maschinelle Lernansätze wie Deep Learning, die Rohdaten über Lernzprozesse direkt verarbeiten können, sind aufgrund der großen Reichweite und der dreidimensionalen Auflösung der gemessenen Punktwolken sehr wichtig. Somit hat sich ein weites Forschungsfeld mit vielen Herausforderungen und ungelösten Problemen etabliert. Diese Arbeit zielt darauf ab, dieses Defizit zu verringern und effiziente Algorithmen zur 3D-Objekterkennung zu entwickeln. Sie stellt ein tiefes Neuronales Netzwerk mit spezifischen Schichten und einer neuartigen Fehlerfunktion zur sicheren Lokalisierung und Schätzung der Orientierung von Objekten aus Punktwolken bereit. Zunächst wird ein 3D-Detektor entwickelt, der in nur einem Vorwärtsdurchlauf aus einer Punktwolke alle Objekte detektiert. Anschließend wird dieser Detektor durch die Fusion von komplementären semantischen Merkmalen aus Kamerabildern und einem gemeinsamen probabilistischen Tracking verfeinert, um die Detektionen zu stabilisieren und Ausreißer zu filtern. Im letzten Teil wird ein Konzept für den Einsatz in einem bestehenden Testfahrzeug vorgestellt, das sich auf die halbautomatische Generierung eines geeigneten Datensatzes konzentriert. Hierbei wird eine Auswertung auf Daten von Automotive-Lidaren vorgestellt. Als Alternative zur zielgerichteten künstlichen Datengenerierung wird ein weiteres generatives Neuronales Netzwerk untersucht. Experimente mit den erzeugten anwendungsspezifischen- und Benchmark-Datensätzen zeigen, dass sich die vorgestellten Methoden mit dem Stand der Technik messen können und gleichzeitig auf Effizienz für den Einsatz in selbstfahrenden Autos optimiert sind. Darüber hinaus enthalten sie einen umfangreichen Satz an Evaluierungsmetriken und -ergebnissen, die eine solide Grundlage für die zukünftige Forschung bilden

Dense Visual Simultaneous Localisation and Mapping in Collaborative and Outdoor Scenarios

Dense visual simultaneous localisation and mapping (SLAM) systems can produce 3D reconstructions that are digital facsimiles of the physical space they describe. Systems that can produce dense maps with this level of fidelity in real time provide foundational spatial reasoning capabilities for many downstream tasks in autonomous robotics. Over the past 15 years, mapping small scale, indoor environments, such as desks and buildings, with a single slow moving, hand-held sensor has been one of the central focuses of dense visual SLAM research. However, most dense visual SLAM systems exhibit a number of limitations which mean they cannot be directly applied in collaborative or outdoors settings. The contribution of this thesis is to address these limitations with the development of new systems and algorithms for collaborative dense mapping, efficient dense alternation and outdoors operation with fast camera motion and wide field of view (FOV) cameras. We use ElasticFusion, a state-of-the-art dense SLAM system, as our starting point where each of these contributions is implemented as a novel extension to the system. We first present a collaborative dense SLAM system that allows a number of cameras starting with unknown initial relative positions to maintain local maps with the original ElasticFusion algorithm. Visual place recognition across local maps results in constraints that allow maps to be aligned into a common global reference frame, facilitating collaborative mapping and tracking of multiple cameras within a shared map. Within dense alternation based SLAM systems, the standard approach is to fuse every frame into the dense model without considering whether the information contained within the frame is already captured by the dense map and therefore redundant. As the number of cameras or the scale of the map increases, this approach becomes inefficient. In our second contribution, we address this inefficiency by introducing a novel information theoretic approach to keyframe selection that allows the system to avoid processing redundant information. We implement the procedure within ElasticFusion, demonstrating a marked reduction in the number of frames required by the system to estimate an accurate, denoised surface reconstruction. Before dense SLAM techniques can be applied in outdoor scenarios we must first address their reliance on active depth cameras, and their lack of suitability to fast camera motion. In our third contribution we present an outdoor dense SLAM system. The system overcomes the need for an active sensor by employing neural network-based depth inference to predict the geometry of the scene as it appears in each image. To address the issue of camera tracking during fast motion we employ a hybrid architecture, combining elements of both dense and sparse SLAM systems to perform camera tracking and to achieve globally consistent dense mapping. Automotive applications present a particularly important setting for dense visual SLAM systems. Such applications are characterised by their use of wide FOV cameras and are therefore not accurately modelled by the standard pinhole camera model. The fourth contribution of this thesis is to extend the above hybrid sparse-dense monocular SLAM system to cater for large FOV fisheye imagery. This is achieved by reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. To estimate depth, we introduce a new version of the PackNet depth estimation neural network (Guizilini et al., 2020) adapted for fisheye inputs. To demonstrate the effectiveness of our contributions, we present experimental results, computed by processing the synthetic ICL-NUIM dataset of Handa et al. (2014) as well as the real-world TUM-RGBD dataset of Sturm et al. (2012). For outdoor SLAM we show the results of our system processing the autonomous driving KITTI and KITTI-360 datasets of Geiger et al. (2012a) and Liao et al. (2021) respectively

Multi-task near-field perception for autonomous driving using surround-view fisheye cameras

Die Bildung der Augen führte zum Urknall der Evolution. Die Dynamik änderte sich von einem primitiven Organismus, der auf den Kontakt mit der Nahrung wartete, zu einem Organismus, der durch visuelle Sensoren gesucht wurde. Das menschliche Auge ist eine der raffiniertesten Entwicklungen der Evolution, aber es hat immer noch Mängel. Der Mensch hat über Millionen von Jahren einen biologischen Wahrnehmungsalgorithmus entwickelt, der in der Lage ist, Autos zu fahren, Maschinen zu bedienen, Flugzeuge zu steuern und Schiffe zu navigieren. Die Automatisierung dieser Fähigkeiten für Computer ist entscheidend für verschiedene Anwendungen, darunter selbstfahrende Autos, Augmented Realität und architektonische Vermessung. Die visuelle Nahfeldwahrnehmung im Kontext von selbstfahrenden Autos kann die Umgebung in einem Bereich von 0 - 10 Metern und 360° Abdeckung um das Fahrzeug herum wahrnehmen. Sie ist eine entscheidende Entscheidungskomponente bei der Entwicklung eines sichereren automatisierten Fahrens. Jüngste Fortschritte im Bereich Computer Vision und Deep Learning in Verbindung mit hochwertigen Sensoren wie Kameras und LiDARs haben ausgereifte Lösungen für die visuelle Wahrnehmung hervorgebracht. Bisher stand die Fernfeldwahrnehmung im Vordergrund. Ein weiteres wichtiges Problem ist die begrenzte Rechenleistung, die für die Entwicklung von Echtzeit-Anwendungen zur Verfügung steht. Aufgrund dieses Engpasses kommt es häufig zu einem Kompromiss zwischen Leistung und Laufzeiteffizienz. Wir konzentrieren uns auf die folgenden Themen, um diese anzugehen: 1) Entwicklung von Nahfeld-Wahrnehmungsalgorithmen mit hoher Leistung und geringer Rechenkomplexität für verschiedene visuelle Wahrnehmungsaufgaben wie geometrische und semantische Aufgaben unter Verwendung von faltbaren neuronalen Netzen. 2) Verwendung von Multi-Task-Learning zur Überwindung von Rechenengpässen durch die gemeinsame Nutzung von initialen Faltungsschichten zwischen den Aufgaben und die Entwicklung von Optimierungsstrategien, die die Aufgaben ausbalancieren.The formation of eyes led to the big bang of evolution. The dynamics changed from a primitive organism waiting for the food to come into contact for eating food being sought after by visual sensors. The human eye is one of the most sophisticated developments of evolution, but it still has defects. Humans have evolved a biological perception algorithm capable of driving cars, operating machinery, piloting aircraft, and navigating ships over millions of years. Automating these capabilities for computers is critical for various applications, including self-driving cars, augmented reality, and architectural surveying. Near-field visual perception in the context of self-driving cars can perceive the environment in a range of 0 - 10 meters and 360° coverage around the vehicle. It is a critical decision-making component in the development of safer automated driving. Recent advances in computer vision and deep learning, in conjunction with high-quality sensors such as cameras and LiDARs, have fueled mature visual perception solutions. Until now, far-field perception has been the primary focus. Another significant issue is the limited processing power available for developing real-time applications. Because of this bottleneck, there is frequently a trade-off between performance and run-time efficiency. We concentrate on the following issues in order to address them: 1) Developing near-field perception algorithms with high performance and low computational complexity for various visual perception tasks such as geometric and semantic tasks using convolutional neural networks. 2) Using Multi-Task Learning to overcome computational bottlenecks by sharing initial convolutional layers between tasks and developing optimization strategies that balance tasks

Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning

Autonomous agents in any environment require accurate and reliable position and motion estimation to complete their required tasks. Many different sensor modalities have been utilized for this task such as GPS, ultra-wide band, visual simultaneous localization and mapping (SLAM), and light detection and ranging (LiDAR) SLAM. Many of the traditional positioning systems do not take advantage of the recent advances in the machine learning field. In this work, an omnidirectional camera position estimation system relying primarily on a learned model is presented. The positioning system benefits from the wide field of view provided by an omnidirectional camera. Recent developments in the self-supervised learning field for generating useful features from unlabeled data are also assessed. A novel radial patch pretext task for omnidirectional images is presented in this work. The resulting implementation will be a robot localization and tracking algorithm that can be adapted to a variety of environments such as warehouses and college campuses. Further experiments with additional types of sensors including 3D LiDAR, 60 GHz wireless, and Ultra-Wideband localization systems utilizing machine learning are also explored. A fused learned localization model utilizing multiple sensor modalities is evaluated in comparison to individual sensor models