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Dealing with visual perception in robots and its applications to manipulation and imitation, this monograph focuses on stereo-based methods and systems for object recognition and 6 DoF pose estimation as well as for marker-less human motion capture.
This book provides an overview of model-based environmental visual perception for humanoid robots. The visual perception of a humanoid robot creates a bidirectional bridge connecting sensor signals with internal representations of environmental objects. The objective of such perception systems is to answer two fundamental questions: What & where is it? To answer these questions using a sensor-to-representation bridge, coordinated processes are conducted to extract and exploit cues matching robot’s mental representations to physical entities. These include sensor & actuator modeling, calibration, filtering, and feature extraction for state estimation. This book discusses the following topics in depth: • Active Sensing: Robust probabilistic methods for optimal, high dynamic range image acquisition are suitable for use with inexpensive cameras. This enables ideal sensing in arbitrary environmental conditions encountered in human-centric spaces. The book quantitatively shows the importance of equipping robots with dependable visual sensing. • Feature Extraction & Recognition: Parameter-free, edge extraction methods based on structural graphs enable the representation of geometric primitives effectively and efficiently. This is done by eccentricity segmentation providing excellent recognition even on noisy & low-resolution images. Stereoscopic vision, Euclidean metric and graph-shape descriptors are shown to be powerful mechanisms for difficult recognition tasks. • Global Self-Localization & Depth Uncertainty Learning: Simultaneous feature matching for global localization and 6D self-pose estimation are addressed by a novel geometric and probabilistic concept using intersection of Gaussian spheres. The path from intuition to the closed-form optimal solution determining the robot location is described, including a supervised learning method for uncertainty depth modeling based on extensive ground-truth training data from a motion capture system. The methods and experiments are presented in self-contained chapters with comparisons and the state of the art. The algorithms were implemented and empirically evaluated on two humanoid robots: ARMAR III-A & B. The excellent robustness, performance and derived results received an award at the IEEE conference on humanoid robots and the contributions have been utilized for numerous visual manipulation tasks with demonstration at distinguished venues such as ICRA, CeBIT, IAS, and Automatica.
Humanoid robots are highly sophisticated machines equipped with human-like sensory and motor capabilities. Today we are on the verge of a new era of rapid transformations in both science and engineering-one that brings together technological advancements in a way that will accelerate both neuroscience and robotics. Humanoid Robotics and Neuroscienc
In this book, the capability map, a novel general representation of the kinematic capabilities of a robot arm, is introduced. The capability map allows to determine how well regions of the workspace are reachable for the end effector in different orientations. It is a representation that can be machine processed as well as intuitively visualized for the human. The capability map and the derived algorithms are a valuable source of information for high- and low-level planning processes. The versatile applicability of the capability map is shown by examples from several distinct application domains. In human-robot interaction, a bi-manual interface for tele-operation is objectively evaluated. In low-level geometric planning, more human-like motion is planned for a humanoid robot while also reducing the computation time. And in high-level task reasoning, the suitability of a robot for a task is evaluated.
The increasing presence of mobile robots in our everyday lives introduces the requirements for their intelligent and autonomous features. Therefore the next generation of mobile robots should be more self-capable, in respect to: increasing of their functionality in unforeseen situations, decreasing of the human involvement in their everyday operations and their maintenance; being robust; fault tolerant and reliable in their operation. Although mobile robotic systems have been a topic of research for decades and aside the technology improvements nowadays, the subject on how to program and making them more autonomous in their operations is still an open field for research. Applying bio-inspired, organic approaches in robotics domain is one of the methodologies that are considered that would help on making the robots more autonomous and self-capable, i.e. having properties such as: self-reconfiguration, self-adaptation, self-optimization, etc. In this book several novel biologically inspired approaches for walking robots (multi-legged and humanoid) domain are introduced and elaborated. They are related to self-organized and self-stabilized robot walking, anomaly detection within robot systems using self-adaptation, and mitigating the faulty robot conditions by self-reconfiguration of a multi-legged walking robot. The approaches presented have been practically evaluated in various test scenarios, the results from the experiments are discussed in details and their practical usefulness is validated.
This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions.
This book constitutes the refereed proceedings of the 18th International Multimedia Modeling Conference, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised regular papers, 12 special session papers, 15 poster session papers, and 6 demo session papers were carefully reviewed and selected from 142 submissions. The papers are organized in the following topical sections: annotation, annotation and interactive multimedia applications, event and activity, mining and mobile multimedia applications, search, summarization and visualization, visualization and advanced multimedia systems, and the special sessions: interactive and immersive entertainment and communication, multimedia preservation: how to ensure multimedia access over time, multi-modal and cross-modal search, and video surveillance.
One of the most distinguishing abilities that human beings display is the ability of turning almost everything into a clue to make a problem affordable in relation to what one knows and, most of all, to what one does not know. That is what characterizes humans as chance seekers. A poor pattern of reasoning and even our ignorance may help us make a decision, and eventually solve a problem. This is the rationale of biased rationality. However, not everything leads us always to a good decision. Some people are not satisfied with weak arguments or it-is-just-so strategies. They want something better. This second attitude points to a different form of rationality that takes advantage of the idea of distributed cognition. Basically, human beings improve their survival strategies by building cognitive niches capable of delivering potentially ever more symptomatic information. It is through various manipulations of the environment that we gain new and more reliable chances which can be used to de-bias our rationality. Through the laborious activity of cognitive niche construction, we come up with situations in which we are better afforded by our environment, and thus biases or fallacies cease to be appealing.
This book examines the evolution of self-organised multicellular structures, and the remarkable transition from unicellular to multicellular life. It shows the way forward in developing new robotic entities that are versatile, cooperative and self-configuring.
This book moves toward the realization of domestic robots by presenting an integrated view of computer vision and robotics, covering fundamental topics including optimal sensor design, visual servo-ing, 3D object modelling and recognition, and multi-cue tracking, emphasizing robustness throughout. Covering theory and implementation, experimental results and comprehensive multimedia support including video clips, VRML data, C++ code and lecture slides, this book is a practical reference for roboticists and a valuable teaching resource.