REFORM: Rotor Estimation From Object Resampling and Matching

Abstract: In this paper we tackle the problem of correspondence and rotor estimation between models composed of geometric primitives of different types. We frame this problem as searching for the rotor that takes a query model to a reference model. The situations that we consider are those in which our query model: contains additional primitives not present in the reference; is missing primitives that are present in the reference. We will also look at cases in which there are a large number of primitives per model. These are all common issues facing any SLAM-type (simultaneous localisation and mapping) systems. To overcome these problems we introduce an inter-object rotor magnitude-based matching function and a subsampled iterative rotor estimation and matching algorithm. We title the finished algorithm: Rotor Estimation From Object Resampling and Matching—REFORM. REFORM builds on ideas from the RANSAC (RAndom SAmple Consensus) [7] and ICP (Iterative Closest Point) [3, 11] algorithms and extends these to multivector correspondence. It is easily parallelisable and designed for good convergence performance with models of real objects

Geometric Algebra for Optimal Control with Applications in Manipulation Tasks

Many problems in robotics are fundamentally problems of geometry, which lead to an increased research effort in geometric methods for robotics in recent years. The results were algorithms using the various frameworks of screw theory, Lie algebra and dual quaternions. A unification and generalization of these popular formalisms can be found in geometric algebra. The aim of this paper is to showcase the capabilities of geometric algebra when applied to robot manipulation tasks. In particular the modelling of cost functions for optimal control can be done uniformly across different geometric primitives leading to a low symbolic complexity of the resulting expressions and a geometric intuitiveness. We demonstrate the usefulness, simplicity and computational efficiency of geometric algebra in several experiments using a Franka Emika robot. The presented algorithms were implemented in c++20 and resulted in the publicly available library \textit{gafro}. The benchmark shows faster computation of the kinematics than state-of-the-art robotics libraries.Comment: 16 pages, 13 figures

Monocular Pose Estimation Based on Global and Local Features

The presented thesis work deals with several mathematical and practical aspects of the monocular pose estimation problem. Pose estimation means to estimate the position and orientation of a model object with respect to a camera used as a sensor element. Three main aspects of the pose estimation problem are considered. These are the model representations, correspondence search and pose computation. Free-form contours and surfaces are considered for the approaches presented in this work. The pose estimation problem and the global representation of free-form contours and surfaces are defined in the mathematical framework of the conformal geometric algebra (CGA), which allows a compact and linear modeling of the monocular pose estimation scenario. Additionally, a new local representation of these entities is presented which is also defined in CGA. Furthermore, it allows the extraction of local feature information of these models in 3D space and in the image plane. This local information is combined with the global contour information obtained from the global representations in order to improve the pose estimation algorithms. The main contribution of this work is the introduction of new variants of the iterative closest point (ICP) algorithm based on the combination of local and global features. Sets of compatible model and image features are obtained from the proposed local model representation of free-form contours. This allows to translate the correspondence search problem onto the image plane and to use the feature information to develop new correspondence search criteria. The structural ICP algorithm is defined as a variant of the classical ICP algorithm with additional model and image structural constraints. Initially, this new variant is applied to planar 3D free-form contours. Then, the feature extraction process is adapted to the case of free-form surfaces. This allows to define the correlation ICP algorithm for free-form surfaces. In this case, the minimal Euclidean distance criterion is replaced by a feature correlation measure. The addition of structural information in the search process results in better conditioned correspondences and therefore in a better computed pose. Furthermore, global information (position and orientation) is used in combination with the correlation ICP to simplify and improve the pre-alignment approaches for the monocular pose estimation. Finally, all the presented approaches are combined to handle the pose estimation of surfaces when partial occlusions are present in the image. Experiments made on synthetic and real data are presented to demonstrate the robustness and behavior of the new ICP variants in comparison with standard approaches

Konforme geometrische Algebra und deren Anwendungen auf stochastische Optimierungsprobleme im Bereich 3D-Vision

In the present work, the modeling capabilities of conformal geometric algebra (CGA) are harnessed to approach typical problems from the research field of 3D-vision. This increasingly popular methodology is then extended in a new fashion by the integration of a least squares technique into the framework of CGA. Specifically, choosing the linear Gauss-Helmert model as the basis, the most general variant of least squares adjustment can be brought into operation. The result is a new versatile parameter estimation, termed GH-method, that reconciles two different mathematical areas, that is algebra and stochastics, under the umbrella of geometry. The main concern of the thesis is to show up the advantages inhering with this combination. Monocular pose estimation, from the subject 3D-vision, is the applicational focus of this thesis; given a picture of a scene, position and orientation of the image capturing vision system with respect to an external coordinate system define the pose. The developed parameter estimation technique is applied to different variants of this problem. Parameters are encoded by the algebra elements, called multivectors. They can be geometric objects as a circle, geometric operators as a rotation or likewise the pose. In the conducted pose experiments, observations are image pixels with associated uncertainties. The high accuracy achieved throughout all experiments confirms the competitiveness of the proposed estimation technique. Central to this work is also the consideration of omnidirectional vision using a paracatadioptric imaging sensor. It is demonstrated that CGA provides the ideal framework to model the related image formation. Two variants of the perspective pose estimation problem are adapted to the omnidirectional case. A new formalization of the epipolar geometry of two images in terms of CGA is developed, from which new insights into the structures behind the essential and the fundamental matrix, respectively, are drawn. Renowned standard approaches are shown to implicitly make use of CGA. Finally, an invocation of the GH-method for estimating epipoles is presented. Experimental results substantiate the goodness of this approach. Next to the detailed elucidations on parameter estimation, this text also gives a comprehensive introduction to geometric algebra, its tensor representation, the conformal space and the respective conformal geometric algebra. A valuable contribution is especially the analytic investigation into the geometric capabilities of CGA.Die vorliegende Arbeit ist motiviert durch die im Forschungszweig Computer Vision (CV) der Informatik typisch auftretenden geometrischen Problemstellungen auf der Grundlage von digitalen Bildaufnahmen. Hierzu zählt die Berechnung einer optimal durch eine Menge von Bildpunkten verlaufende Kurve, die Bestimmung der Epipolargeometrie, das Schätzen der Pose eines Objektes oder die 3D-Rekonstruktion. Diese Klasse von Problemen lässt sich durch den Einsatz der geometrischen Algebra (GA) – so werden unter geometrischen Aspekten besonders interessante Clifford Algebren bezeichnet – in überaus prägnanter und geschlossener Form modellieren. Dieser mit wachsender Akzeptanz verfolgte Ansatz, der beständig durch den Lehrstuhl „Kognitive Systeme“ der Universität Kiel weiterentwickelt wird, ist zentraler Bestandteile der Dissertation. Speziell wird die „konforme geometrische Algebra“ (CGA), die auf einer nicht-linearen Einbettung des euklidischen 3D-Raumes in einen fünfdimensionalen projektiven konformen Raum beruht, eingesetzt. Die Elemente dieser Algebra erlauben die Repräsentation geometrischer Basisentitäten, im wesentlichen Punkte, Linien, Kreise, Kugeln und Ebenen. Eine Vielzahl von Operationen ist möglich; besonders interessant sind die Transformationen der enthaltenen konformen Gruppe sowie die Möglichkeit algebraisch mit Unterräumen zu rechnen, d.h. diese zu vergrößern, zu schneiden oder Inzidenzen abzufragen. Den zweiten wichtigen Bestandteil der Arbeit stellt ein für die oben genannten Problemstellungen typisches stochastischen Verfahren dar – die Ausgleichsrechnung nach der Methode der kleinsten Quadrate. Deren allgemeinste Form erwächst aus der Verwendung des aus der Geodäsie bekannten linearen Gauß-Helmert (GH) Modells. Der resultierende GH-Schätzer zeigt alle Optimalitätseigenschaften wie minimale Varianz und Erwartungstreue. Eine der geometrischen Algebra inhärente Tensordarstellung stellt eine geeignete numerische Schnittstelle zwischen CGA und der GH-Schätzmethode zur Verfügung. Aufgrund der Bilinearität des Algebraprodukts lässt sich so ebenfalls das Konzept der Fehlerfortpflanzung, ein wichtiges Instrument der Ausgleichsrechnung, mit hoher Genauigkeit auf die Operationen der Algebra ausdehnen. Im Ergebnis entsteht ein neues universelles Parameterschätzverfahren zur Bestimmung der des jeweiligen Problems zugrundeliegenden Variablen. Ziel der vorliegenden Arbeit ist es auch, die aus der Verbindung von Algebra und Stochastik entstehenden Vorteile anhand von typischen CV-Anwendungen herauszustellen. Den Schwerpunkt hierfür bildet die Schätzung der Pose (Position und Orientierung eines Objekts bezüglich eines objektfremden Koordinatensystems), z.B. die eines Roboters anhand eines vom Roboter aufgenommenen Kamerabildes. Es wird ebenfalls gezeigt, dass CGA den optimalen Rahmen zur Modellierung omnidirektionaler Bildgebungsverfahren bietet, falls diese auf einem katadioptrischen System mit parabolischem Spiegel beruhen. Als omnidirektionale Anwendungen werden Posenschätzung sowie die Bestimmung der Epipolargeometrie präsentiert. Die erreichte Güte der GH-Parameterschätzung in den einzelnen Anwendungen wird jeweils durch experimentell gewonnene Resultate untermauert. Neben den umfangreichen Ausführungen zur Parameterschätzung liefert diese Arbeit auch eine detaillierte Einführung und Herleitung der geometrischen Algebra. Besonderes Augenmerk ist auch auf die analytische Darlegung der konformen geometrischen Algebra zu richten

Monocular pose estimation based on global and local features

The presented thesis work deals with several mathematical and practical aspects of the monocular pose estimation problem. Pose estimation means to estimate the position and orientation of a model object with respect to a camera used as a sensor element. Threemain aspects of the pose estimation problem are considered. These are themodel representations, correspondence search and pose computation. Free-form contours and surfaces are considered for the approaches presented in this work. The pose estimation problem and the global representation of free-form contours and surfaces are defined in the mathematical framework of the conformal geometric algebra (CGA), which allows a compact and linear modeling of the monocular pose estimation scenario. Additionally, a new local representation of these entities is presented which is also defined in CGA. Furthermore, it allows the extraction of local feature information of these models in 3D space and in the image plane. This local information is combined with the global contour information obtained from the global representations in order to improve the pose estimation algorithms. The main contribution of this work is the introduction of new variants of the iterative closest point (ICP) algorithm based on the combination of local and global features. Sets of compatible model and image features are obtained from the proposed local model representation of free-form contours. This allows to translate the correspondence search problem onto the image plane and to use the feature information to develop new correspondence search criteria. The structural ICP algorithm is defined as a variant of the classical ICP algorithm with additional model and image structural constraints. Initially, this new variant is applied to planar 3D free-form contours. Then, the feature extraction process is adapted to the case of free-form surfaces. This allows to define the correlation ICP algorithm for free-form surfaces. In this case, the minimal Euclidean distance criterion is replaced by a feature correlation measure. The addition of structural information in the search process results in better conditioned correspondences and therefore in a better computed pose. Furthermore, global information (position and orientation) is used in combination with the correlation ICP to simplify and improve the pre-alignment approaches for the monocular pose estimation. Finally, all the presented approaches are combined to handle the pose estimation of surfaces when partial occlusions are present in the image. Experiments made on synthetic and real data are presented to demonstrate the robustness and behavior of the new ICP variants in comparison with standard approaches

Model-Based Environmental Visual Perception for Humanoid Robots

The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Multidisciplinary Design Optimization of Electric Aircraft Considering Systems Modeling and Packaging

Electric aircraft propulsion is an intriguing path towards sustainable aviation, but the technological challenges are significant. Bulky and heavy electrical components such as batteries create spatial integration and aircraft performance challenges, especially for longer-range aircraft. A common thread among all aircraft with electric propulsion is the close coupling of aircraft design disciplines, such as aerodynamics, structures, propulsion, controls, and thermal management. Multidisciplinary design optimization (MDO) is a promising technique for solving design problems with many closely-coupled physical disciplines. The first half of this dissertation focuses on MDO of electric aircraft considering systems modeling. First, design of electric aircraft is reviewed in detail from the perspective of the various disciplines. Next, methods and models for electric aircraft propulsion systems are introduced. A case study involving a general aviation airplane is explored in order to validate the performance of the methods and generate some insight into the tradespace for series hybrid aircraft. The systems modeling approach is then extended to include basic thermal management systems. The prior case study is revisisted while considering thermal constraints. Impact of thermal management on aircraft performance is assessed. The thermal management analysis methods are validated using flight test data from the Pipistrel Velis Electro, finding good agreement between experiment and simulation. Finally, an MDO model of a parallel hybrid electric transport aircraft with a liquid-cooled thermal management system is constructed. Sensitivities of aircraft performance with respect to important technologies parameters are computed. This first half introduces the first publicly-available simulation tool that can handle unsteady thermal states and that offers efficient and accurate gradients. The methods are very efficient, enabling users to perform dozens or hundreds of optimization runs in a short amount of time using modest computational resources. Other novel contributions include the first empirical validation of thermal management models for MDO against real flight test data, as well as the only comprehensive look so far at the unsteady thermal management of a transport-scale parallel hybrid aircraft. The second half of the dissertation introduces novel methods for performing high-fidelity shape optimization studies subject to packaging or spatial integration constraints. A new mathematical formulation for generalized packaging constraints is introduced. The constraint formulation is demonstrated on simple aerodynamic shape optimization test cases. Next, a wing design study involving optimal battery packaging is conducted in order to demonstrate the coupling of outer mold line design and propulsion system component design via spatial integration. Finally, a more complex aerostructural optimization involving the wing of a hydrogen aircraft is constructed and solved. These test cases demonstrate the interdisciplinary coupling introduced by packaging constraints, as well as the impact of spatial integration on aircraft performance. This latter half contributes a powerful new way for MDO engineers to pose realistic spatial constraints in their shape optimization problems, thus solving an important practical barrier to the industrial adoption of MDO for certain relevant problems. This work also represents the first time an MDO problem has been posed and solved for an aircraft using hydrogen fuel in the wing. Altogether, this dissertation significantly advances the state of the art in modeling, simulation, and optimization tools for aircraft with electric propulsion architectures and introduces new insights into the design spaces for several diverse aircraft configurations.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169658/1/bbrelje_1.pd