SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.

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Die Sensordatenfusion ist eine allgegenwärtige Aufgabe im Bereich der mobilen Robotik und darüber hinaus. In der vorliegenden Arbeit wird das typischerweise verwendete Verfahren zur Sensordatenfusion in der Robotik in Frage gestellt und anhand von neuartigen Algorithmen, basierend auf einem Faktorgraphen, gelöst sowie mit einer korrespondierenden Extended-Kalman-Filter-Implementierung verglichen. Im Mittelpunkt steht dabei das technische sowie algorithmische Sensorkonzept für die Navigation eines Flugroboters im Innenbereich. Ausführliche Experimente zeigen die Qualitätssteigerung unter Verwendung der neuen Variante der Sensordatenfusion, aber auch Einschränkungen und Beispiele mit nahezu identischen Ergebnissen beider Varianten der Sensordatenfusion. Neben Experimenten anhand einer hardwarenahen Simulation wird die Funktionsweise auch anhand von realen Hardwaredaten evaluiert.

Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.

This work aims at providing a novel camera motion estimation pipeline from large collections of unordered omnidirectional images. In oder to keep the pipeline as general and flexible as possible, cameras are modelled as unit spheres, allowing to incorporate any central camera type. For each camera an unprojection lookup is generated from intrinsics, which is called P2S-map (Pixel-to-Sphere-map), mapping pixels to their corresponding positions on the unit sphere. Consequently the camera geometry becomes independent of the underlying projection model. The pipeline also generates P2S-maps from world map projections with less distortion effects as they are known from cartography. Using P2S-maps from camera calibration and world map projection allows to convert omnidirectional camera images to an appropriate world map projection in oder to apply standard feature extraction and matching algorithms for data association. The proposed estimation pipeline combines the flexibility of SfM (Structure from Motion) - which handles unordered image collections - with the efficiency of PGO (Pose Graph Optimization), which is used as back-end in graph-based Visual SLAM (Simultaneous Localization and Mapping) approaches to optimize camera poses from large image sequences. SfM uses BA (Bundle Adjustment) to jointly optimize camera poses (motion) and 3d feature locations (structure), which becomes computationally expensive for large-scale scenarios. On the contrary PGO solves for camera poses (motion) from measured transformations between cameras, maintaining optimization managable. The proposed estimation algorithm combines both worlds. It obtains up-to-scale transformations between image pairs using two-view constraints, which are jointly scaled using trifocal constraints. A pose graph is generated from scaled two-view transformations and solved by PGO to obtain camera motion efficiently even for large image collections. Obtained results can be used as input data to provide initial pose estimates for further 3d reconstruction purposes e.g. to build a sparse structure from feature correspondences in an SfM or SLAM framework with further refinement via BA. The pipeline also incorporates fixed extrinsic constraints from multi-camera setups as well as depth information provided by RGBD sensors. The entire camera motion estimation pipeline does not need to generate a sparse 3d structure of the captured environment and thus is called SCME (Structureless Camera Motion Estimation).:1 Introduction 1.1 Motivation 1.1.1 Increasing Interest of Image-Based 3D Reconstruction 1.1.2 Underground Environments as Challenging Scenario 1.1.3 Improved Mobile Camera Systems for Full Omnidirectional Imaging 1.2 Issues 1.2.1 Directional versus Omnidirectional Image Acquisition 1.2.2 Structure from Motion versus Visual Simultaneous Localization and Mapping 1.3 Contribution 1.4 Structure of this Work 2 Related Work 2.1 Visual Simultaneous Localization and Mapping 2.1.1 Visual Odometry 2.1.2 Pose Graph Optimization 2.2 Structure from Motion 2.2.1 Bundle Adjustment 2.2.2 Structureless Bundle Adjustment 2.3 Corresponding Issues 2.4 Proposed Reconstruction Pipeline 3 Cameras and Pixel-to-Sphere Mappings with P2S-Maps 3.1 Types 3.2 Models 3.2.1 Unified Camera Model 3.2.2 Polynomal Camera Model 3.2.3 Spherical Camera Model 3.3 P2S-Maps - Mapping onto Unit Sphere via Lookup Table 3.3.1 Lookup Table as Color Image 3.3.2 Lookup Interpolation 3.3.3 Depth Data Conversion 4 Calibration 4.1 Overview of Proposed Calibration Pipeline 4.2 Target Detection 4.3 Intrinsic Calibration 4.3.1 Selected Examples 4.4 Extrinsic Calibration 4.4.1 3D-2D Pose Estimation 4.4.2 2D-2D Pose Estimation 4.4.3 Pose Optimization 4.4.4 Uncertainty Estimation 4.4.5 PoseGraph Representation 4.4.6 Bundle Adjustment 4.4.7 Selected Examples 5 Full Omnidirectional Image Projections 5.1 Panoramic Image Stitching 5.2 World Map Projections 5.3 World Map Projection Generator for P2S-Maps 5.4 Conversion between Projections based on P2S-Maps 5.4.1 Proposed Workflow 5.4.2 Data Storage Format 5.4.3 Real World Example 6 Relations between Two Camera Spheres 6.1 Forward and Backward Projection 6.2 Triangulation 6.2.1 Linear Least Squares Method 6.2.2 Alternative Midpoint Method 6.3 Epipolar Geometry 6.4 Transformation Recovery from Essential Matrix 6.4.1 Cheirality 6.4.2 Standard Procedure 6.4.3 Simplified Procedure 6.4.4 Improved Procedure 6.5 Two-View Estimation 6.5.1 Evaluation Strategy 6.5.2 Error Metric 6.5.3 Evaluation of Estimation Algorithms 6.5.4 Concluding Remarks 6.6 Two-View Optimization 6.6.1 Epipolar-Based Error Distances 6.6.2 Projection-Based Error Distances 6.6.3 Comparison between Error Distances 6.7 Two-View Translation Scaling 6.7.1 Linear Least Squares Estimation 6.7.2 Non-Linear Least Squares Optimization 6.7.3 Comparison between Initial and Optimized Scaling Factor 6.8 Homography to Identify Degeneracies 6.8.1 Homography for Spherical Cameras 6.8.2 Homography Estimation 6.8.3 Homography Optimization 6.8.4 Homography and Pure Rotation 6.8.5 Homography in Epipolar Geometry 7 Relations between Three Camera Spheres 7.1 Three View Geometry 7.2 Crossing Epipolar Planes Geometry 7.3 Trifocal Geometry 7.4 Relation between Trifocal, Three-View and Crossing Epipolar Planes 7.5 Translation Ratio between Up-To-Scale Two-View Transformations 7.5.1 Structureless Determination Approaches 7.5.2 Structure-Based Determination Approaches 7.5.3 Comparison between Proposed Approaches 8 Pose Graphs 8.1 Optimization Principle 8.2 Solvers 8.2.1 Additional Graph Solvers 8.2.2 False Loop Closure Detection 8.3 Pose Graph Generation 8.3.1 Generation of Synthetic Pose Graph Data 8.3.2 Optimization of Synthetic Pose Graph Data 9 Structureless Camera Motion Estimation 9.1 SCME Pipeline 9.2 Determination of Two-View Translation Scale Factors 9.3 Integration of Depth Data 9.4 Integration of Extrinsic Camera Constraints 10 Camera Motion Estimation Results 10.1 Directional Camera Images 10.2 Omnidirectional Camera Images 11 Conclusion 11.1 Summary 11.2 Outlook and Future Work Appendices A.1 Additional Extrinsic Calibration Results A.2 Linear Least Squares Scaling A.3 Proof Rank Deficiency A.4 Alternative Derivation Midpoint Method A.5 Simplification of Depth Calculation A.6 Relation between Epipolar and Circumferential Constraint A.7 Covariance Estimation A.8 Uncertainty Estimation from Epipolar Geometry A.9 Two-View Scaling Factor Estimation: Uncertainty Estimation A.10 Two-View Scaling Factor Optimization: Uncertainty Estimation A.11 Depth from Adjoining Two-View Geometries A.12 Alternative Three-View Derivation A.12.1 Second Derivation Approach A.12.2 Third Derivation Approach A.13 Relation between Trifocal Geometry and Alternative Midpoint Method A.14 Additional Pose Graph Generation Examples A.15 Pose Graph Solver Settings A.16 Additional Pose Graph Optimization Examples Bibliography

Die motorischen Fähigkeiten humanoider Roboter werden häufig von antriebsbedingten Nichtlinearitäten und Reibungseffekten negativ beeinflusst. Zur deren Kompensation werden üblicherweise modellbasierte Regelkreise genutzt, die i.d.R. von einer hochfrequenten Signalverarbeitung und mehreren Sensorqualitäten abhängen. Entgegen solch modellbasierten Techniken werden in der vorliegenden Arbeit modellfreie Steuerungsmethoden auf Basis parallel gekoppelter Antriebe entwickelt. Zur Entwicklung und Untersuchung dieser Steuerungsmethoden wird nach der von Pfeifer in seinem Werk “How the body shapes the way we think” beschriebenen synthetischen Methodik vorgegangen. Entgegen modellbasierten Untersuchungen auf Basis von Simulationen stehen bei der synthetischen Methodik empirische Untersuchungen am realen System im Vordergrund. Als Ausgangspunkt dienen konventionelle elektromechanische Antriebe mit deren bekannten leistungseinschränkenden Nichtlinearitäten und Reibungseffekten. Durch die parallele Kopplung mehrerer Antriebe an einem einzelnen Gelenk wird das Spektrum der Steuerungsmöglichkeiten deutlich erweitert. Es zeigt sich, dass (1) durch eine konstante antagonistische Vorspannung das Arbeitsverhalten von konventionellen Proportionalreglern optimiert werden kann, (2) durch dynamische asymmetrische Änderung der Vorspannung Nichtlinearitäten bei niedrigen Geschwindigkeiten ausgeglichen werden können und (3) getriebebedingte Reibungseffekte mit einer phasenverschobenen Pulsmodulation der Steuersignale kompensiert werden können. Weiterhin wird gezeigt, wie die erarbeiteten Steuerungsmethoden auf beliebig viele parallel gekoppelte Antriebe übertragen werden können. Für den praktischen Einsatz der Steuerungsmethoden werden diese in einer hybriden Steuerung zusammengeführt. Diese wird durch eine weitere Funktion, den Energiesparmodus beim Halten statischer Positionen, ergänzt und am humanoiden Roboter Myon implementiert und experimentell evaluiert. / Motor functions of humanoid robots are often negatively influenced by nonlinearities and friction effects of the actuators. The popular means of compensation are control circuits based on modelling, which rely on powerful HF Signal processing and various sensor qualities. In contrast, this thesis develops non-modelling control methods based on parallel coupled actuators. Development and exploration of these control methods follow Pfeifer’s synthetic methodology as described in his work “How the body shapes the way we think”. In contrast to the analysis based on emulation as used in modelling, the synthetic methodology focuses rather on empirical tests within the real system. The present work explores control methods for parallel coupled actuators for use in robot points. It starts from conventional electromechanical actuators with their known power limiting nonlinearities and frictional effects. Linking several parallel coupled actuators to a single joint significantly expands the spectrum of control capabilities. Using two parallel coupled actuators as an example, it is examined to which extent undesirable properties of single actuators can be compensated. The results show that (1) the Performance of conventional proportional controllers can be optimized by a constant antagonistic bias voltage, (2) nonlinearities at low velocities can be balanced out by a dynamic asymmetrical adjustment of the bias, and that (3) gear related frictional effects can be compensated by a phase shifted pulse modulation of the control signals. In addition, it is shown how the developed control methods can be applied to a random number of parallel coupled actuators. For practical use, the various control methods are combined in a hybrid control, which is supplemented by an energy saving mode when maintaining static positions. The hybrid control is being implemented into the humanoid robot Myon and evaluated by experiment.

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This thesis provides a systematic guideline for evaluating, integrating and designing the collaborative end effector system associated with commercially available collaborative robots (cobots). On the basis of ISO/TS 15066:2016, customers’ demands are categorised as a macro demand framework and micro demand variables, which are parameterised and hierarchised. By analysing these parameters, the collaborative degree is determined to correlate the hazards with protection measures, which can demonstrate the collaborative safety level transparently. After that, the safety protection measures are specifically proposed corresponding to the potential risks. Finally, an industrial application is evaluated, a novel adapter utilised on the UR5 cobot is illustrated, and its characteristics for the collaborative operation are described.:1 Introduction 2 Robotic end effector in Assembly and Handling 3 Concept and Definition of Collaboration 4 Statement of the academic purpose and methodology 5 Guideline and Methodology for designing the Active Lightweight End-effector (ALE) for collaborative robots 6 Performance demand d4): multiplicity and versatility of end effector 7 A safety monitoring end effector (Adapter) for collaborative operations 8 A practical study of collaborative integration based on collaborative degree 9 Conclusion Appendix / Diese Arbeit liefert einen systematischen Leitfaden für die Bewertung, Integration und Gestaltung des kollaborierenden Endeffektorsystems in Verbindung mit kommerziell verfügbaren kollaborativen Robotern (Kobots). Auf der Grundlage der ISO/TS 15066:2016 werden die Anforderungen der Kunden in einen Makro-Bedarfsrahmen und Mikro-Bedarfsvariablen kategorisiert, die parametrisiert und hierarchisiert werden. Durch die Analyse dieser Parameter wird der Kollaborationsgrad ermittelt, um die Gefährdungen mit Schutzmaßnahmen zu korrelieren, die die kollaborierende Sicherheitsstufe transparent darstellen können. Danach werden die Schutzmaßnahmen entsprechend den potenziellen Risiken vorgeschlagen. Schließlich wird ein industrieller Anwendungsfall bewertet, ein neuartiger Adapter für den Einsatz an einem Roboter UR5 erläutert und dessen Kenndaten für den kollaborierenden Betrieb beschrieben.:1 Introduction 2 Robotic end effector in Assembly and Handling 3 Concept and Definition of Collaboration 4 Statement of the academic purpose and methodology 5 Guideline and Methodology for designing the Active Lightweight End-effector (ALE) for collaborative robots 6 Performance demand d4): multiplicity and versatility of end effector 7 A safety monitoring end effector (Adapter) for collaborative operations 8 A practical study of collaborative integration based on collaborative degree 9 Conclusion Appendix

3 mal im Jahr erscheinende Zeitschrift über aktuelle Themen der TU Chemnitz

3 mal im Jahr erscheinende Zeitschrift über aktuelle Themen der TU Chemnitz