Erscheinungsdatum: 11/2009, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: SLAM Using Monocular Vision and Inertial Measurements, Titelzusatz: A New Low-cost Approach for Portable Simultaneous Localization and Mapping, Autor: Franzini, Simone, Verlag: VDM Verlag, Sprache: Englisch, Rubrik: Informatik // EDV, Sonstiges, Seiten: 80, Informationen: Paperback, Gewicht: 136 gr, Verkäufer: averdo
Control for Navigation of a Mobile Robot Using Monocular Data ab 68 € als Taschenbuch: Local Model Predictive Control for Navigation of a Wheeled Mobile Robot Using Monocular Information. Aus dem Bereich: Bücher, Wissenschaft, Technik,
SLAM Using Monocular Vision and Inertial Measurements ab 48.99 € als Taschenbuch: A New Low-cost Approach for Portable Simultaneous Localization and Mapping. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,
Control for Navigation of a Mobile Robot Using Monocular Data ab 68 EURO Local Model Predictive Control for Navigation of a Wheeled Mobile Robot Using Monocular Information
SLAM Using Monocular Vision and Inertial Measurements ab 48.99 EURO A New Low-cost Approach for Portable Simultaneous Localization and Mapping
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.
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.
Developing on-board driver assistance systems (DAS) requires understanding of various events involving the motions of the vehicles in the vicinity of the host vehicle. Determining the position of other vehicles on the road is a key information to help driver assistance systems. Thus, robust and reliable vehicle detection and tracking are the basic steps in these systems. Since monocular vision based systems are particularly interesting for the advantage of reducing costs and maintenance and for the high fidelity information they give about the driving environment, the problem can be addressed by applying computer vision techniques. This work has mainly been focused on detecting and tracking vehicles viewed from inside a vehicle the camera mounted in daylight conditions. The approach presented in the book uses vehicle shadow clues and vehicle edge information to obtain cost effective and fast estimation. After extracting vehicles, the algorithm effectively track them using a Kalman filter based tracking algorithm. Several sequences from real traffic situations have been tested, obtaining highly accurate multiple vehicle detections.