Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Perception of Unstructured Environments for Autonomous Off-Road Vehicles

Autonome Fahrzeuge benötigen die Fähigkeit zur Perzeption als eine notwendige Voraussetzung für eine kontrollierbare und sichere Interaktion, um ihre Umgebung wahrzunehmen und zu verstehen. Perzeption für strukturierte Innen- und Außenumgebungen deckt wirtschaftlich lukrative Bereiche, wie den autonomen Personentransport oder die Industrierobotik ab, während die Perzeption unstrukturierter Umgebungen im Forschungsfeld der Umgebungswahrnehmung stark unterrepräsentiert ist. Die analysierten unstrukturierten Umgebungen stellen eine besondere Herausforderung dar, da die vorhandenen, natürlichen und gewachsenen Geometrien meist keine homogene Struktur aufweisen und ähnliche Texturen sowie schwer zu trennende Objekte dominieren. Dies erschwert die Erfassung dieser Umgebungen und deren Interpretation, sodass Perzeptionsmethoden speziell für diesen Anwendungsbereich konzipiert und optimiert werden müssen. In dieser Dissertation werden neuartige und optimierte Perzeptionsmethoden für unstrukturierte Umgebungen vorgeschlagen und in einer ganzheitlichen, dreistufigen Pipeline für autonome Geländefahrzeuge kombiniert: Low-Level-, Mid-Level- und High-Level-Perzeption. Die vorgeschlagenen klassischen Methoden und maschinellen Lernmethoden (ML) zur Perzeption bzw.~Wahrnehmung ergänzen sich gegenseitig. Darüber hinaus ermöglicht die Kombination von Perzeptions- und Validierungsmethoden für jede Ebene eine zuverlässige Wahrnehmung der möglicherweise unbekannten Umgebung, wobei lose und eng gekoppelte Validierungsmethoden kombiniert werden, um eine ausreichende, aber flexible Bewertung der vorgeschlagenen Perzeptionsmethoden zu gewährleisten. Alle Methoden wurden als einzelne Module innerhalb der in dieser Arbeit vorgeschlagenen Perzeptions- und Validierungspipeline entwickelt, und ihre flexible Kombination ermöglicht verschiedene Pipelinedesigns für eine Vielzahl von Geländefahrzeugen und Anwendungsfällen je nach Bedarf. Low-Level-Perzeption gewährleistet eine eng gekoppelte Konfidenzbewertung für rohe 2D- und 3D-Sensordaten, um Sensorausfälle zu erkennen und eine ausreichende Genauigkeit der Sensordaten zu gewährleisten. Darüber hinaus werden neuartige Kalibrierungs- und Registrierungsansätze für Multisensorsysteme in der Perzeption vorgestellt, welche lediglich die Struktur der Umgebung nutzen, um die erfassten Sensordaten zu registrieren: ein halbautomatischer Registrierungsansatz zur Registrierung mehrerer 3D~Light Detection and Ranging (LiDAR) Sensoren und ein vertrauensbasiertes Framework, welches verschiedene Registrierungsmethoden kombiniert und die Registrierung verschiedener Sensoren mit unterschiedlichen Messprinzipien ermöglicht. Dabei validiert die Kombination mehrerer Registrierungsmethoden die Registrierungsergebnisse in einer eng gekoppelten Weise. Mid-Level-Perzeption ermöglicht die 3D-Rekonstruktion unstrukturierter Umgebungen mit zwei Verfahren zur Schätzung der Disparität von Stereobildern: ein klassisches, korrelationsbasiertes Verfahren für Hyperspektralbilder, welches eine begrenzte Menge an Test- und Validierungsdaten erfordert, und ein zweites Verfahren, welches die Disparität aus Graustufenbildern mit neuronalen Faltungsnetzen (CNNs) schätzt. Neuartige Disparitätsfehlermetriken und eine Evaluierungs-Toolbox für die 3D-Rekonstruktion von Stereobildern ergänzen die vorgeschlagenen Methoden zur Disparitätsschätzung aus Stereobildern und ermöglichen deren lose gekoppelte Validierung. High-Level-Perzeption konzentriert sich auf die Interpretation von einzelnen 3D-Punktwolken zur Befahrbarkeitsanalyse, Objekterkennung und Hindernisvermeidung. Eine Domänentransferanalyse für State-of-the-art-Methoden zur semantischen 3D-Segmentierung liefert Empfehlungen für eine möglichst exakte Segmentierung in neuen Zieldomänen ohne eine Generierung neuer Trainingsdaten. Der vorgestellte Trainingsansatz für 3D-Segmentierungsverfahren mit CNNs kann die benötigte Menge an Trainingsdaten weiter reduzieren. Methoden zur Erklärbarkeit künstlicher Intelligenz vor und nach der Modellierung ermöglichen eine lose gekoppelte Validierung der vorgeschlagenen High-Level-Methoden mit Datensatzbewertung und modellunabhängigen Erklärungen für CNN-Vorhersagen. Altlastensanierung und Militärlogistik sind die beiden Hauptanwendungsfälle in unstrukturierten Umgebungen, welche in dieser Arbeit behandelt werden. Diese Anwendungsszenarien zeigen auch, wie die Lücke zwischen der Entwicklung einzelner Methoden und ihrer Integration in die Verarbeitungskette für autonome Geländefahrzeuge mit Lokalisierung, Kartierung, Planung und Steuerung geschlossen werden kann. Zusammenfassend lässt sich sagen, dass die vorgeschlagene Pipeline flexible Perzeptionslösungen für autonome Geländefahrzeuge bietet und die begleitende Validierung eine exakte und vertrauenswürdige Perzeption unstrukturierter Umgebungen gewährleistet

Semantic Mapping of Road Scenes

The problem of understanding road scenes has been on the fore-front in the computer vision community for the last couple of years. This enables autonomous systems to navigate and understand the surroundings in which it operates. It involves reconstructing the scene and estimating the objects present in it, such as ‘vehicles’, ‘road’, ‘pavements’ and ‘buildings’. This thesis focusses on these aspects and proposes solutions to address them. First, we propose a solution to generate a dense semantic map from multiple street-level images. This map can be imagined as the bird’s eye view of the region with associated semantic labels for ten’s of kilometres of street level data. We generate the overhead semantic view from street level images. This is in contrast to existing approaches using satellite/overhead imagery for classification of urban region, allowing us to produce a detailed semantic map for a large scale urban area. Then we describe a method to perform large scale dense 3D reconstruction of road scenes with associated semantic labels. Our method fuses the depth-maps in an online fashion, generated from the stereo pairs across time into a global 3D volume, in order to accommodate arbitrarily long image sequences. The object class labels estimated from the street level stereo image sequence are used to annotate the reconstructed volume. Then we exploit the scene structure in object class labelling by performing inference over the meshed representation of the scene. By performing labelling over the mesh we solve two issues: Firstly, images often have redundant information with multiple images describing the same scene. Solving these images separately is slow, where our method is approximately a magnitude faster in the inference stage compared to normal inference in the image domain. Secondly, often multiple images, even though they describe the same scene result in inconsistent labelling. By solving a single mesh, we remove the inconsistency of labelling across the images. Also our mesh based labelling takes into account of the object layout in the scene, which is often ambiguous in the image domain, thereby increasing the accuracy of object labelling. Finally, we perform labelling and structure computation through a hierarchical robust PN Markov Random Field defined on voxels and super-voxels given by an octree. This allows us to infer the 3D structure and the object-class labels in a principled manner, through bounded approximate minimisation of a well defined and studied energy functional. In this thesis, we also introduce two object labelled datasets created from real world data. The 15 kilometre Yotta Labelled dataset consists of 8,000 images per camera view of the roadways of the United Kingdom with a subset of them annotated with object class labels and the second dataset is comprised of ground truth object labels for the publicly available KITTI dataset. Both the datasets are available publicly and we hope will be helpful to the vision research community

Supervised learning and inference of semantic information from road scene images

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

Supervised learning and inference of semantic information from road scene images

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

Use of Consumer-grade Depth Cameras in Mobile Robot Navigation

Simultaneous Localization And Mapping (SLAM) stands as one of the core techniques used by robots for autonomous navigation. Cameras combining Red-Green-Blue (RGB) color information and depth (D) information are called RGB-D cameras or depth cam- eras. RGB-D cameras can provide rich information for indoor mobile robot navigation. Microsoft’s Kinect device, a representative low cost RGB-D camera product, has attracted tremendous attention from researchers in recent years, for its relatively high quality of depth measurement. By analyzing the multi-data stream of both color and depth, better 3D plane detectors, local shape registration techniques can be designed to improve the quality of mobile robot navigation. In the first part of this work, models of the Kinect’s cameras and projector are es- tablished, which can be applied for calibration and characterization of the Kinect device. Experiments show both variable depth resolution and Kinect’s own optical noises in depth values calculation. Based on Kinect’s models and characterization, this project implements an optimized 3D matching system for SLAM, from processing of RGB-D data to further algorithms design. The developed system includes the following parts: (1) raw data pre- processing and de-noising, improving the quality of integrated environment depth maps. (2) 3D planes surfaces detection and fitting with RANSAC algorithms; also providing ap- plications and illustrative examples about multi-scale-multi-planes detections algorithms which designed for common indoor environment. The proposed approach is validated on scene and object reconstruction. RGB-D features matching under uncertainty and noise in a large scale of data, forms the basis of future application in mobile robot naviga- tion. Experimental results have shown that system performance improvement is valid and feasible

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available