Efficient Dense Registration, Segmentation, and Modeling Methods for RGB-D Environment Perception

One perspective for artificial intelligence research is to build machines that perform tasks autonomously in our complex everyday environments. This setting poses challenges to the development of perception skills: A robot should be able to perceive its location and objects in its surrounding, while the objects and the robot itself could also be moving. Objects may not only be composed of rigid parts, but could be non-rigidly deformable or appear in a variety of similar shapes. Furthermore, it could be relevant to the task to observe object semantics. For a robot acting fluently and immediately, these perception challenges demand efficient methods. This theses presents novel approaches to robot perception with RGB-D sensors. It develops efficient registration, segmentation, and modeling methods for scene and object perception. We propose multi-resolution surfel maps as a concise representation for RGB-D measurements. We develop probabilistic registration methods that handle rigid scenes, scenes with multiple rigid parts that move differently, and scenes that undergo non-rigid deformations. We use these methods to learn and perceive 3D models of scenes and objects in both static and dynamic environments. For learning models of static scenes, we propose a real-time capable simultaneous localization and mapping approach. It aligns key views in RGB-D video using our rigid registration method and optimizes the pose graph of the key views. The acquired models are then perceived in live images through detection and tracking within a Bayesian filtering framework. An assumption frequently made for environment mapping is that the observed scene remains static during the mapping process. Through rigid multi-body registration, we take advantage of releasing this assumption: Our registration method segments views into parts that move independently between the views and simultaneously estimates their motion. Within simultaneous motion segmentation, localization, and mapping, we separate scenes into objects by their motion. Our approach acquires 3D models of objects and concurrently infers hierarchical part relations between them using probabilistic reasoning. It can be applied for interactive learning of objects and their part decomposition. Endowing robots with manipulation skills for a large variety of objects is a tedious endeavor if the skill is programmed for every instance of an object class. Furthermore, slight deformations of an instance could not be handled by an inflexible program. Deformable registration is useful to perceive such shape variations, e.g., between specific instances of a tool. We develop an efficient deformable registration method and apply it for the transfer of robot manipulation skills between varying object instances. On the object-class level, we segment images using random decision forest classifiers in real-time. The probabilistic labelings of individual images are fused in 3D semantic maps within a Bayesian framework. We combine our object-class segmentation method with simultaneous localization and mapping to achieve online semantic mapping in real-time. The methods developed in this thesis are evaluated in experiments on publicly available benchmark datasets and novel own datasets. We publicly demonstrate several of our perception approaches within integrated robot systems in the mobile manipulation context.Effiziente Dichte Registrierungs-, Segmentierungs- und Modellierungsmethoden für die RGB-D Umgebungswahrnehmung In dieser Arbeit beschäftigen wir uns mit Herausforderungen der visuellen Wahrnehmung für intelligente Roboter in Alltagsumgebungen. Solche Roboter sollen sich selbst in ihrer Umgebung zurechtfinden, und Wissen über den Verbleib von Objekten erwerben können. Die Schwierigkeit dieser Aufgaben erhöht sich in dynamischen Umgebungen, in denen ein Roboter die Bewegung einzelner Teile differenzieren und auch wahrnehmen muss, wie sich diese Teile bewegen. Bewegt sich ein Roboter selbständig in dieser Umgebung, muss er auch seine eigene Bewegung von der Veränderung der Umgebung unterscheiden. Szenen können sich aber nicht nur durch die Bewegung starrer Teile verändern. Auch die Teile selbst können ihre Form in nicht-rigider Weise ändern. Eine weitere Herausforderung stellt die semantische Interpretation von Szenengeometrie und -aussehen dar. Damit intelligente Roboter unmittelbar und flüssig handeln können, sind effiziente Algorithmen für diese Wahrnehmungsprobleme erforderlich. Im ersten Teil dieser Arbeit entwickeln wir effiziente Methoden zur Repräsentation und Registrierung von RGB-D Messungen. Zunächst stellen wir Multi-Resolutions-Oberflächenelement-Karten (engl. multi-resolution surfel maps, MRSMaps) als eine kompakte Repräsentation von RGB-D Messungen vor, die unseren effizienten Registrierungsmethoden zugrunde liegt. Bilder können effizient in dieser Repräsentation aggregiert werde, wobei auch mehrere Bilder aus verschiedenen Blickpunkten integriert werden können, um Modelle von Szenen und Objekte aus vielfältigen Ansichten darzustellen. Für die effiziente, robuste und genaue Registrierung von MRSMaps wird eine Methode vorgestellt, die Rigidheit der betrachteten Szene voraussetzt. Die Registrierung schätzt die Kamerabewegung zwischen den Bildern und gewinnt ihre Effizienz durch die Ausnutzung der kompakten multi-resolutionalen Darstellung der Karten. Die Registrierungsmethode erzielt hohe Bildverarbeitungsraten auf einer CPU. Wir demonstrieren hohe Effizienz, Genauigkeit und Robustheit unserer Methode im Vergleich zum bisherigen Stand der Forschung auf Vergleichsdatensätzen. In einem weiteren Registrierungsansatz lösen wir uns von der Annahme, dass die betrachtete Szene zwischen Bildern statisch ist. Wir erlauben nun, dass sich rigide Teile der Szene bewegen dürfen, und erweitern unser rigides Registrierungsverfahren auf diesen Fall. Unser Ansatz segmentiert das Bild in Bereiche einzelner Teile, die sich unterschiedlich zwischen Bildern bewegen. Wir demonstrieren hohe Segmentierungsgenauigkeit und Genauigkeit in der Bewegungsschätzung unter Echtzeitbedingungen für die Verarbeitung. Schließlich entwickeln wir ein Verfahren für die Wahrnehmung von nicht-rigiden Deformationen zwischen zwei MRSMaps. Auch hier nutzen wir die multi-resolutionale Struktur in den Karten für ein effizientes Registrieren von grob zu fein. Wir schlagen Methoden vor, um aus den geschätzten Deformationen die lokale Bewegung zwischen den Bildern zu berechnen. Wir evaluieren Genauigkeit und Effizienz des Registrierungsverfahrens. Der zweite Teil dieser Arbeit widmet sich der Verwendung unserer Kartenrepräsentation und Registrierungsmethoden für die Wahrnehmung von Szenen und Objekten. Wir verwenden MRSMaps und unsere rigide Registrierungsmethode, um dichte 3D Modelle von Szenen und Objekten zu lernen. Die räumlichen Beziehungen zwischen Schlüsselansichten, die wir durch Registrierung schätzen, werden in einem Simultanen Lokalisierungs- und Kartierungsverfahren (engl. simultaneous localization and mapping, SLAM) gegeneinander abgewogen, um die Blickposen der Schlüsselansichten zu schätzen. Für das Verfolgen der Kamerapose bezüglich der Modelle in Echtzeit, kombinieren wir die Genauigkeit unserer Registrierung mit der Robustheit von Partikelfiltern. Zu Beginn der Posenverfolgung, oder wenn das Objekt aufgrund von Verdeckungen oder extremen Bewegungen nicht weiter verfolgt werden konnte, initialisieren wir das Filter durch Objektdetektion. Anschließend wenden wir unsere erweiterten Registrierungsverfahren für die Wahrnehmung in nicht-rigiden Szenen und für die Übertragung von Objekthandhabungsfähigkeiten von Robotern an. Wir erweitern unseren rigiden Kartierungsansatz auf dynamische Szenen, in denen sich rigide Teile bewegen. Die Bewegungssegmente in Schlüsselansichten werden zueinander in Bezug gesetzt, um Äquivalenz- und Teilebeziehungen von Objekten probabilistisch zu inferieren, denen die Segmente entsprechen. Auch hier liefert unsere Registrierungsmethode die Bewegung der Kamera bezüglich der Objekte, die wir in einem SLAM Verfahren optimieren. Aus diesen Blickposen wiederum können wir die Bewegungssegmente in dichten Objektmodellen vereinen. Objekte einer Klasse teilen oft eine gemeinsame Topologie von funktionalen Elementen, die durch Formkorrespondenzen ermittelt werden kann. Wir verwenden unsere deformierbare Registrierung, um solche Korrespondenzen zu finden und die Handhabung eines Objektes durch einen Roboter auf neue Objektinstanzen derselben Klasse zu übertragen. Schließlich entwickeln wir einen echtzeitfähigen Ansatz, der Kategorien von Objekten in RGB-D Bildern erkennt und segmentiert. Die Segmentierung basiert auf Ensemblen randomisierter Entscheidungsbäume, die Geometrie- und Texturmerkmale zur Klassifikation verwenden. Wir fusionieren Segmentierungen von Einzelbildern einer Szene aus mehreren Ansichten in einer semantischen Objektklassenkarte mit Hilfe unseres SLAM-Verfahrens. Die vorgestellten Methoden werden auf öffentlich verfügbaren Vergleichsdatensätzen und eigenen Datensätzen evaluiert. Einige unserer Ansätze wurden auch in integrierten Robotersystemen für mobile Objekthantierungsaufgaben öffentlich demonstriert. Sie waren ein wichtiger Bestandteil für das Gewinnen der RoboCup-Roboterwettbewerbe in der RoboCup@Home Liga in den Jahren 2011, 2012 und 2013

Efficient 3D Segmentation, Registration and Mapping for Mobile Robots

Sometimes simple is better! For certain situations and tasks, simple but robust methods can achieve the same or better results in the same or less time than related sophisticated approaches. In the context of robots operating in real-world environments, key challenges are perceiving objects of interest and obstacles as well as building maps of the environment and localizing therein. The goal of this thesis is to carefully analyze such problem formulations, to deduce valid assumptions and simplifications, and to develop simple solutions that are both robust and fast. All approaches make use of sensors capturing 3D information, such as consumer RGBD cameras. Comparative evaluations show the performance of the developed approaches. For identifying objects and regions of interest in manipulation tasks, a real-time object segmentation pipeline is proposed. It exploits several common assumptions of manipulation tasks such as objects being on horizontal support surfaces (and well separated). It achieves real-time performance by using particularly efficient approximations in the individual processing steps, subsampling the input data where possible, and processing only relevant subsets of the data. The resulting pipeline segments 3D input data with up to 30Hz. In order to obtain complete segmentations of the 3D input data, a second pipeline is proposed that approximates the sampled surface, smooths the underlying data, and segments the smoothed surface into coherent regions belonging to the same geometric primitive. It uses different primitive models and can reliably segment input data into planes, cylinders and spheres. A thorough comparative evaluation shows state-of-the-art performance while computing such segmentations in near real-time. The second part of the thesis addresses the registration of 3D input data, i.e., consistently aligning input captured from different view poses. Several methods are presented for different types of input data. For the particular application of mapping with micro aerial vehicles where the 3D input data is particularly sparse, a pipeline is proposed that uses the same approximate surface reconstruction to exploit the measurement topology and a surface-to-surface registration algorithm that robustly aligns the data. Optimization of the resulting graph of determined view poses then yields globally consistent 3D maps. For sequences of RGBD data this pipeline is extended to include additional subsampling steps and an initial alignment of the data in local windows in the pose graph. In both cases, comparative evaluations show a robust and fast alignment of the input data

Autonomous navigation for guide following in crowded indoor environments

The requirements for assisted living are rapidly changing as the number of elderly patients over the age of 60 continues to increase. This rise places a high level of stress on nurse practitioners who must care for more patients than they are capable. As this trend is expected to continue, new technology will be required to help care for patients. Mobile robots present an opportunity to help alleviate the stress on nurse practitioners by monitoring and performing remedial tasks for elderly patients. In order to produce mobile robots with the ability to perform these tasks, however, many challenges must be overcome. The hospital environment requires a high level of safety to prevent patient injury. Any facility that uses mobile robots, therefore, must be able to ensure that no harm will come to patients whilst in a care environment. This requires the robot to build a high level of understanding about the environment and the people with close proximity to the robot. Hitherto, most mobile robots have used vision-based sensors or 2D laser range finders. 3D time-of-flight sensors have recently been introduced and provide dense 3D point clouds of the environment at real-time frame rates. This provides mobile robots with previously unavailable dense information in real-time. I investigate the use of time-of-flight cameras for mobile robot navigation in crowded environments in this thesis. A unified framework to allow the robot to follow a guide through an indoor environment safely and efficiently is presented. Each component of the framework is analyzed in detail, with real-world scenarios illustrating its practical use. Time-of-flight cameras are relatively new sensors and, therefore, have inherent problems that must be overcome to receive consistent and accurate data. I propose a novel and practical probabilistic framework to overcome many of the inherent problems in this thesis. The framework fuses multiple depth maps with color information forming a reliable and consistent view of the world. In order for the robot to interact with the environment, contextual information is required. To this end, I propose a region-growing segmentation algorithm to group points based on surface characteristics, surface normal and surface curvature. The segmentation process creates a distinct set of surfaces, however, only a limited amount of contextual information is available to allow for interaction. Therefore, a novel classifier is proposed using spherical harmonics to differentiate people from all other objects. The added ability to identify people allows the robot to find potential candidates to follow. However, for safe navigation, the robot must continuously track all visible objects to obtain positional and velocity information. A multi-object tracking system is investigated to track visible objects reliably using multiple cues, shape and color. The tracking system allows the robot to react to the dynamic nature of people by building an estimate of the motion flow. This flow provides the robot with the necessary information to determine where and at what speeds it is safe to drive. In addition, a novel search strategy is proposed to allow the robot to recover a guide who has left the field-of-view. To achieve this, a search map is constructed with areas of the environment ranked according to how likely they are to reveal the guide’s true location. Then, the robot can approach the most likely search area to recover the guide. Finally, all components presented are joined to follow a guide through an indoor environment. The results achieved demonstrate the efficacy of the proposed components

Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them. In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion. In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions. Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporally-coherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification. Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster. In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences. Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology. Experiments on both synthetic and real sequences of dense voxel-set data are shown. This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model constructionComment: 31 pages, 26 figure

Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty

This work proposes a robust visual odometry method for structured environments that combines point features with line and plane segments, extracted through an RGB-D camera. Noisy depth maps are processed by a probabilistic depth fusion framework based on Mixtures of Gaussians to denoise and derive the depth uncertainty, which is then propagated throughout the visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are used to model the uncertainties of the feature parameters and pose is estimated by combining the three types of primitives based on their uncertainties. Performance evaluation on RGB-D sequences collected in this work and two public RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth fusion framework and combining the three feature-types, particularly in scenes with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34 page

Efficient Belief Propagation for Perception and Manipulation in Clutter

Autonomous service robots are required to perform tasks in common human indoor environments. To achieve goals associated with these tasks, the robot should continually perceive, reason its environment, and plan to manipulate objects, which we term as goal-directed manipulation. Perception remains the most challenging aspect of all stages, as common indoor environments typically pose problems in recognizing objects under inherent occlusions with physical interactions among themselves. Despite recent progress in the field of robot perception, accommodating perceptual uncertainty due to partial observations remains challenging and needs to be addressed to achieve the desired autonomy. In this dissertation, we address the problem of perception under uncertainty for robot manipulation in cluttered environments using generative inference methods. Specifically, we aim to enable robots to perceive partially observable environments by maintaining an approximate probability distribution as a belief over possible scene hypotheses. This belief representation captures uncertainty resulting from inter-object occlusions and physical interactions, which are inherently present in clutterred indoor environments. The research efforts presented in this thesis are towards developing appropriate state representations and inference techniques to generate and maintain such belief over contextually plausible scene states. We focus on providing the following features to generative inference while addressing the challenges due to occlusions: 1) generating and maintaining plausible scene hypotheses, 2) reducing the inference search space that typically grows exponentially with respect to the number of objects in a scene, 3) preserving scene hypotheses over continual observations. To generate and maintain plausible scene hypotheses, we propose physics informed scene estimation methods that combine a Newtonian physics engine within a particle based generative inference framework. The proposed variants of our method with and without a Monte Carlo step showed promising results on generating and maintaining plausible hypotheses under complete occlusions. We show that estimating such scenarios would not be possible by the commonly adopted 3D registration methods without the notion of a physical context that our method provides. To scale up the context informed inference to accommodate a larger number of objects, we describe a factorization of scene state into object and object-parts to perform collaborative particle-based inference. This resulted in the Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) algorithm that caters to the demands of the high-dimensional multimodal nature of cluttered scenes while being computationally tractable. We demonstrate that PMPNBP is orders of magnitude faster than the state-of-the-art Nonparametric Belief Propagation method. Additionally, we show that PMPNBP successfully estimates poses of articulated objects under various simulated occlusion scenarios. To extend our PMPNBP algorithm for tracking object states over continuous observations, we explore ways to propose and preserve hypotheses effectively over time. This resulted in an augmentation-selection method, where hypotheses are drawn from various proposals followed by the selection of a subset using PMPNBP that explained the current state of the objects. We discuss and analyze our augmentation-selection method with its counterparts in belief propagation literature. Furthermore, we develop an inference pipeline for pose estimation and tracking of articulated objects in clutter. In this pipeline, the message passing module with the augmentation-selection method is informed by segmentation heatmaps from a trained neural network. In our experiments, we show that our proposed pipeline can effectively maintain belief and track articulated objects over a sequence of observations under occlusion.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163159/1/kdesingh_1.pd

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Bridging the gap between reconstruction and synthesis

Aplicat embargament des de la data de defensa fins el 15 de gener de 20223D reconstruction and image synthesis are two of the main pillars in computer vision. Early works focused on simple tasks such as multi-view reconstruction and texture synthesis. With the spur of Deep Learning, the field has rapidly progressed, making it possible to achieve more complex and high level tasks. For example, the 3D reconstruction results of traditional multi-view approaches are currently obtained with single view methods. Similarly, early pattern based texture synthesis works have resulted in techniques that allow generating novel high-resolution images. In this thesis we have developed a hierarchy of tools that cover all these range of problems, lying at the intersection of computer vision, graphics and machine learning. We tackle the problem of 3D reconstruction and synthesis in the wild. Importantly, we advocate for a paradigm in which not everything should be learned. Instead of applying Deep Learning naively we propose novel representations, layers and architectures that directly embed prior 3D geometric knowledge for the task of 3D reconstruction and synthesis. We apply these techniques to problems including scene/person reconstruction and photo-realistic rendering. We first address methods to reconstruct a scene and the clothed people in it while estimating the camera position. Then, we tackle image and video synthesis for clothed people in the wild. Finally, we bridge the gap between reconstruction and synthesis under the umbrella of a unique novel formulation. Extensive experiments conducted along this thesis show that the proposed techniques improve the performance of Deep Learning models in terms of the quality of the reconstructed 3D shapes / synthesised images, while reducing the amount of supervision and training data required to train them. In summary, we provide a variety of low, mid and high level algorithms that can be used to incorporate prior knowledge into different stages of the Deep Learning pipeline and improve performance in tasks of 3D reconstruction and image synthesis.La reconstrucció 3D i la síntesi d'imatges són dos dels pilars fonamentals en visió per computador. Els estudis previs es centren en tasques senzilles com la reconstrucció amb informació multi-càmera i la síntesi de textures. Amb l'aparició del "Deep Learning", aquest camp ha progressat ràpidament, fent possible assolir tasques molt més complexes. Per exemple, per obtenir una reconstrucció 3D, tradicionalment s'utilitzaven mètodes multi-càmera, en canvi ara, es poden obtenir a partir d'una sola imatge. De la mateixa manera, els primers treballs de síntesi de textures basats en patrons han donat lloc a tècniques que permeten generar noves imatges completes en alta resolució. En aquesta tesi, hem desenvolupat una sèrie d'eines que cobreixen tot aquest ventall de problemes, situats en la intersecció entre la visió per computador, els gràfics i l'aprenentatge automàtic. Abordem el problema de la reconstrucció i la síntesi 3D en el món real. És important destacar que defensem un paradigma on no tot s'ha d'aprendre. Enlloc d'aplicar el "Deep Learning" de forma naïve, proposem representacions novedoses i arquitectures que incorporen directament els coneixements geomètrics ja existents per a aconseguir la reconstrucció 3D i la síntesi d'imatges. Nosaltres apliquem aquestes tècniques a problemes com ara la reconstrucció d'escenes/persones i a la renderització d'imatges fotorealistes. Primer abordem els mètodes per reconstruir una escena, les persones vestides que hi ha i la posició de la càmera. A continuació, abordem la síntesi d'imatges i vídeos de persones vestides en situacions quotidianes. I finalment, aconseguim, a través d'una nova formulació única, connectar la reconstrucció amb la síntesi. Els experiments realitzats al llarg d'aquesta tesi demostren que les tècniques proposades milloren el rendiment dels models de "Deepp Learning" pel que fa a la qualitat de les reconstruccions i les imatges sintetitzades alhora que redueixen la quantitat de dades necessàries per entrenar-los. En resum, proporcionem una varietat d'algoritmes de baix, mitjà i alt nivell que es poden utilitzar per incorporar els coneixements previs a les diferents etapes del "Deep Learning" i millorar el rendiment en tasques de reconstrucció 3D i síntesi d'imatges.Postprint (published version