Erscheinungsdatum: 22.11.2015, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Visual Scene Understanding from Mobile Platforms, Titelzusatz: A Monocular Approach, Autor: Wojek, Christian, Verlag: Südwestdeutscher Verlag für Hochschulschriften AG Co. KG, Sprache: Englisch, Rubrik: Informatik // EDV, Sonstiges, Seiten: 168, Informationen: Paperback, Gewicht: 267 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
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,
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,
Simultaneous Localization and Mapping, comprising estimation of robot ego-motion and building a map of the surrounding environment, is one of the most fundamental tasks of mobile robotics. Many SLAM systems proposed in the past make use of the Global Positioning System (GPS), which renders them both expensive and overly dependent on the presence of the GPS signal. We propose an alternative, low-cost approach for portable SLAM which is based on monocular vision, a promising technique due to its flexibility, ease of use, and ease of calibration. In order to perform this task we use an Extended Kalman Filter, one of the most efficient and robust methods used in SLAM systems. We show how it is possible to improve the estimated position and reduce its uncertainty by fusing data from different sensors, in particular using a simple 3-axis accelerometer. We prove, through careful and intelligent selection and tuning of image analysis algorithms, that real-time, low-cost SLAM is feasible. This work is useful to professionals developing SLAM systems and to people in the larger field of computer vision, especially those interested in feature detection and tracking.
This book introduces a new strategy for mobile robot navigation. The complete navigation strategy is based on local landmark detection. However, the work developed just shows the local navigation results. In this context, local artificial potential fields are used as a way to attract the mobile robot towards a local goal that can act as a passage point and a featured landmark. In order to acomplish with the desired objective, simple perception system and reactive control behaviours were implemented and tested. Concretely, a single on-robot camera system was used to infer the closer robot environment where free aproaching path was computed. Moreover, the proposed control strategy is based on on-line model predictive control techniques where only short prediction horizons are considered for dealing with reactive behaviours and dynamic environments.
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.
Visual scene understanding is one of the ultimate goals in computer vision and has been in the field's focus since its early beginning. Despite continuous effort over several years, applications such as autonomous driving and robotics are still subject to active research. In recent years, improved probabilistic methods became a popular tool for state-of-the-art computer vision algorithms. Additionally, high resolution digital imaging devices and increased computational power became available. By leveraging these methodical and technical advancements current methods obtain encouraging results in well defined environments for robust object class detection, tracking and pixel-wise semantic scene labeling and give rise to renewed hope for further progress in scene understanding for real environments. This book improves state-of-the-art scene understanding with monocular cameras and aims for applications on mobile platforms such as service robots or driver assistance for automotive safety. It develops and improves approaches for object class detection and semantic scene labeling and integrates those into models for global scene reasoning which exploit context at different levels.