Erscheinungsdatum: 05/2010, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Control for Navigation of a Mobile Robot Using Monocular Data, Titelzusatz: Local Model Predictive Control for Navigation of a Wheeled Mobile Robot Using Monocular Information, Autor: Pacheco, Lluís // Luo, Ningsu // Cufí, Xavier, Verlag: LAP Lambert Acad. Publ., Sprache: Englisch, Rubrik: Maschinenbau // Fertigungstechnik, Seiten: 196, Informationen: Paperback, Gewicht: 308 gr, Verkäufer: averdo
Erscheinungsdatum: 24.01.2015, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: A Fuzzy-Mathematical Model to Motion Detection with Monocular Vision, Titelzusatz: Vision Based Mobile Robots, Autor: Pathirana, Suneth, Verlag: LAP Lambert Academic Publishing, Sprache: Englisch, Rubrik: Informatik // EDV, Sonstiges, Seiten: 156, Informationen: Paperback, Gewicht: 249 gr, Verkäufer: averdo
Monocular Model-Based 3D Tracking of Rigid Objects ab 44.49 € als Taschenbuch: A Survey. Aus dem Bereich: Bücher, English, International, Englische Taschenbücher,
A Fuzzy-Mathematical Model to Motion Detection with Monocular Vision ab 44.99 € als Taschenbuch: Vision Based Mobile Robots. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,
Control for Navigation of a Mobile Robot Using Monocular Data ab 67.99 € als Taschenbuch: Local Model Predictive Control for Navigation of a Wheeled Mobile Robot Using Monocular Information. Aus dem Bereich: Bücher, Wissenschaft, Technik,
Vision based inference for mobile robots in order to avoid obstacles, is one of the most attracted areas for both domains of Computer Vision and Robotics. Computer Vision, more specifically the vision for intelligent machines enables mobile robots to perceive the external world with 'wisdom'. Therefore Vision based obstacle avoidance has become one of the major research areas of Robotics. Estimating the motion path and predicting the motion behavior of a dynamic object with only single camera (monocular vision) is a real challenge. We realized this can be done by analyzing a sequence of image frames extracted from a live video stream. But, these analytic techniques must be extremely fast in real time processing, since the decision drawn within reasonably short response time is the only 'god' to safeguard the robot & ensure the safety of others in environment! Therefore, we postulate a fuzzy-mathematical model: an Artificial Intelligence approach to achieve the ultimate objective, which has a significant impact in terms of simplicity (reduced complexity) together with efficiency (minimized computational overhead: resource consumption), rather than conventional mathematical modeling.
Networked 3D virtual environments allow multiple users to interact with each other over the Internet. Users can share some sense of telepresence by remotely animating an avatar that represents them. However, avatar control may be tedious and still render user gestures poorly. This work aims at animating a user s avatar from real time 3D motion capture by monocular computer vision, thus allowing virtual telepresence to anyone using a personal computer with a webcam. The approach followed consists of registering a 3D articulated upper-body model to a video sequence. The first contribution of this work is a method of allocating computing iterations under real-time constrain that achieves optimal robustness and accuracy. The major issue for robust 3D tracking from monocular images is the 3D/2D ambiguities that result from the lack of depth information. As a second contribution, this work enhances particle filtering for 3D/2D registration under limited computation constrains with a number of heuristics, the contribution of which is demonstrated experimentally. A parameterization of the arm pose based on their end-effector is proposed to better model uncertainty in the depth direction.
Vision-based control of wheeled mobile robots is an interesting field of research from a scientific and even social point of view due to its potential applicability. This book presents a formal treatment of some aspects of control theory applied to the problem of vision-based pose regulation of wheeled mobile robots. In this problem, the robot has to reach a desired position and orientation, which are specified by a target image. It is faced in such a way that vision and control are unified to achieve stability of the closed loop, a large region of convergence, without local minima and good robustness against parametric uncertainty. Three different control schemes that rely on monocular vision as unique sensor are presented and evaluated experimentally. A common benefit of these approaches is that they are valid for imaging systems obeying approximately a central projection model, e.g., conventional cameras, catadioptric systems and some fisheye cameras. Thus, the presented control schemes are generic approaches. A minimum set of visual measurements, integrated in adequate task functions, are taken from a geometric constraint imposed between corresponding image features. Particularly, the epipolar geometry and the trifocal tensor are exploited since they can be used for generic scenes. A detailed experimental evaluation is presented for each control scheme.
This book proposes a complete pipeline for monocular (single camera) based 3D mapping of terrestrial and underwater environments. The aim is to provide a solution to large-scale scene modeling that is both accurate and efficient. To this end, we have developed a novel Structure from Motion algorithm that increases mapping accuracy by registering camera views directly with the maps. The camera registration uses a dual approach that adapts to the type of environment being mapped.In order to further increase the accuracy of the resulting maps, a new method is presented, allowing detection of images corresponding to the same scene region (crossovers). Crossovers then used in conjunction with global alignment methods in order to highly reduce estimation errors, especially when mapping large areas. Our method is based on Visual Bag of Words paradigm (BoW), offering a more efficient and simpler solution by eliminating the training stage, generally required by state of the art BoW algorithms.Also, towards developing methods for efficient mapping of large areas (especially with costs related to map storage, transmission and rendering in mind), an online 3D model simplification algorithm is proposed. This new algorithm presents the advantage of selecting only those vertices that are geometrically representative for the scene.