An Interactive System for Creating Object Models From Range Data Based on Simulated Annealing

In hazardous applications such as remediation of buried waste and dismantlement of radioactive facilities, robots are an attractive solution. Sensing to recognize and locate objects is a critical need for robotic operations in unstructured environments. An accurate 3-D model of objects in the scene is necessary for efficient high level control of robots. Drawing upon concepts from supervisory control, the authors have developed an interactive system for creating object models from range data, based on simulated annealing. Site modeling is a task that is typically performed using purely manual or autonomous techniques, each of which has inherent strengths and weaknesses. However, an interactive modeling system combines the advantages of both manual and autonomous methods, to create a system that has high operator productivity as well as high flexibility and robustness. The system is unique in that it can work with very sparse range data, tolerate occlusions, and tolerate cluttered scenes. The authors have performed an informal evaluation with four operators on 16 different scenes, and have shown that the interactive system is superior to either manual or automatic methods in terms of task time and accuracy

Part-based Grouping and Recognition: A Model-Guided Approach

Institute of Perception, Action and BehaviourThe recovery of generic solid parts is a fundamental step towards the realization of general-purpose vision systems. This thesis investigates issues in grouping, segmentation and recognition of parts from two-dimensional edge images. A new paradigm of part-based grouping of features is introduced that bridges the classical grouping and model-based approaches with the purpose of directly recovering parts from real images, and part-like models are used that both yield low theoretical complexity and reliably recover part-plausible groups of features. The part-like models used are statistical point distribution models, whose training set is built using random deformable superellipse. The computational approach that is proposed to perform model-guided part-based grouping consists of four distinct stages. In the first stage, codons, contour portions of similar curvature, are extracted from the raw edge image. They are considered to be indivisible image features because they have the desirable property of belonging either to single parts or joints. In the second stage, small seed groups (currently pairs, but further extension are proposed) of codons are found that give enough structural information for part hypotheses to be created. The third stage consists in initialising and pre-shaping the models to all the seed groups and then performing a full fitting to a large neighbourhood of the pre-shaped model. The concept of pre-shaping to a few significant features is a relatively new concept in deformable model fitting that has helped to dramatically increase robustness. The initialisations of the part models to the seed groups is performed by the first direct least-square ellipse fitting algorithm, which has been jointly discovered during this research; a full theoretical proof of the method is provided. The last stage pertains to the global filtering of all the hypotheses generated by the previous stages according to the Minimum Description Length criterion: the small number of grouping hypotheses that survive this filtering stage are the most economical representation of the image in terms of the part-like models. The filtering is performed by the maximisation of a boolean quadratic function by a genetic algorithm, which has resulted in the best trade-off between speed and robustness. Finally, images of parts can have a pronounced 3D structure, with ends or sides clearly visible. In order to recover this important information, the part-based grouping method is extended by employing parametrically deformable aspects models which, starting from the initial position provided by the previous stages, are fitted to the raw image by simulated annealing. These models are inspired by deformable superquadrics but are built by geometric construction, which render them two order of magnitudes faster to generate than in previous works. A large number of experiments is provided that validate the approach and, since several new issues have been opened by it, some future work is proposed

Sense, Think, Grasp: A study on visual and tactile information processing for autonomous manipulation

Interacting with the environment using hands is one of the distinctive abilities of humans with respect to other species. This aptitude reflects on the crucial role played by objects\u2019 manipulation in the world that we have shaped for us. With a view of bringing robots outside industries for supporting people during everyday life, the ability of manipulating objects autonomously and in unstructured environments is therefore one of the basic skills they need. Autonomous manipulation is characterized by great complexity especially regarding the processing of sensors information to perceive the surrounding environment. Humans rely on vision for wideranging tridimensional information, prioprioception for the awareness of the relative position of their own body in the space and the sense of touch for local information when physical interaction with objects happens. The study of autonomous manipulation in robotics aims at transferring similar perceptive skills to robots so that, combined with state of the art control techniques, they could be able to achieve similar performance in manipulating objects. The great complexity of this task makes autonomous manipulation one of the open problems in robotics that has been drawing increasingly the research attention in the latest years. In this work of Thesis, we propose possible solutions to some key components of autonomous manipulation, focusing in particular on the perception problem and testing the developed approaches on the humanoid robotic platform iCub. When available, vision is the first source of information to be processed for inferring how to interact with objects. The object modeling and grasping pipeline based on superquadric functions we designed meets this need, since it reconstructs the object 3D model from partial point cloud and computes a suitable hand pose for grasping the object. Retrieving objects information with touch sensors only is a relevant skill that becomes crucial when vision is occluded, as happens for instance during physical interaction with the object. We addressed this problem with the design of a novel tactile localization algorithm, named Memory Unscented Particle Filter, capable of localizing and recognizing objects relying solely on 3D contact points collected on the object surface. Another key point of autonomous manipulation we report on in this Thesis work is bi-manual coordination. The execution of more advanced manipulation tasks in fact might require the use and coordination of two arms. Tool usage for instance often requires a proper in-hand object pose that can be obtained via dual-arm re-grasping. In pick-and-place tasks sometimes the initial and target position of the object do not belong to the same arm workspace, then requiring to use one hand for lifting the object and the other for locating it in the new position. At this regard, we implemented a pipeline for executing the handover task, i.e. the sequences of actions for autonomously passing an object from one robot hand on to the other. The contributions described thus far address specific subproblems of the more complex task of autonomous manipulation. This actually differs from what humans do, in that humans develop their manipulation skills by learning through experience and trial-and-error strategy. Aproper mathematical formulation for encoding this learning approach is given by Deep Reinforcement Learning, that has recently proved to be successful in many robotics applications. For this reason, in this Thesis we report also on the six month experience carried out at Berkeley Artificial Intelligence Research laboratory with the goal of studying Deep Reinforcement Learning and its application to autonomous manipulation

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

Integrating Vision and Physical Interaction for Discovery, Segmentation and Grasping of Unknown Objects

In dieser Arbeit werden Verfahren der Bildverarbeitung und die Fähigkeit humanoider Roboter, mit ihrer Umgebung physisch zu interagieren, in engem Zusammenspiel eingesetzt, um unbekannte Objekte zu identifizieren, sie vom Hintergrund und anderen Objekten zu trennen, und letztendlich zu greifen. Im Verlauf dieser interaktiven Exploration werden außerdem Eigenschaften des Objektes wie etwa sein Aussehen und seine Form ermittelt

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

Inferring Human Pose and Motion from Images

As optical gesture recognition technology advances, touchless human computer interfaces of the future will soon become a reality. One particular technology, markerless motion capture, has gained a large amount of attention, with widespread application in diverse disciplines, including medical science, sports analysis, advanced user interfaces, and virtual arts. However, the complexity of human anatomy makes markerless motion capture a non-trivial problem: I) parameterised pose configuration exhibits high dimensionality, and II) there is considerable ambiguity in surjective inverse mapping from observation to pose configuration spaces with a limited number of camera views. These factors together lead to multimodality in high dimensional space, making markerless motion capture an ill-posed problem. This study challenges these difficulties by introducing a new framework. It begins with automatically modelling specific subject template models and calibrating posture at the initial stage. Subsequent tracking is accomplished by embedding naturally-inspired global optimisation into the sequential Bayesian filtering framework. Tracking is enhanced by several robust evaluation improvements. Sparsity of images is managed by compressive evaluation, further accelerating computational efficiency in high dimensional space

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Computational Strategies for Object Recognition

This article reviews the available methods forautomated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the s~mplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations Representative examples of various methods are summarized, and the classes of strategies are evaluated with respect to their appropriateness for particular applications