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Deformable Surface 3D Reconstruction from Monocular Images ab 40.49 € als Taschenbuch: . Aus dem Bereich: Bücher, Ratgeber, Computer & Internet,
Deformable Surface 3D Reconstruction from Monocular Images ab 63.99 € als Taschenbuch: . Aus dem Bereich: Bücher, Ratgeber, Computer & Internet,
Depth estimation from a single monocular image is a difficult problem.The task is even more challenging as depth cues such as motion, stereo correspondences are not present in single image. Hence machine learning based approach for extracting depth information from single image is proposed. Firstly depth is generated by manifold learning in which LLE algorithm is used, it is a non linear method of dimensionality reduction in which neighbors of input set in higher dimensional space are preserved while being transformed into lower dimensional space. The depth maps obtained are further refined by fixed point algorithm, it is supervised learning in which those features are extracted from image which have strong correspondences with labels.
We present a visual servoing system for an amphibious legged robot. This is a monocular-vision based servoing mechanism that enables the robot to track and follow a target both underwater and on the ground. We use three different tracking algorithms to track and localize the target in the image, with color being the tracked feature. Tracking is performed based on the color of the target object, target color distribution and color distribution with a probabilistic kernel. Output from the tracker is channeled to a proportional-integral-derivative controller, which generates steering commands for the robot controller. The robot controller in turn takes the steering commands and generates motor commands for the six legs of the robot. A large class of significant applications can be addressed by allowing such a robot to follow a diver or some other moving target. The system has been evaluated in open-water environments and under natural lighting conditions, and has successfully performed tracking and following of a wide variety of target objects.
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 presents a hardware architecture for the Simultaneous Localization And Mapping (SLAM) problem applied to embedded robots. The architecture is composed by highly specialized modules for robot localization and feature-based map building from images obtained directly from CMOS cameras in real time. The system is completely embedded on a Field-Programmable Gate Array (FPGA) device, where several hardware-orientated optimizations are exploited. The main modules of the architecture are the Extended Kalman Filter (EKF) and the feature detection system based on the SIFT (Scale Invariant Feature Transform) algorithm. Additionally, this book also presents basic concepts about mapping and state-of-the-art algorithms for SLAM with monocular and stereo vision.