Publications (4)0 Total impact
Robotics and Autonomous Systems. 01/2007; 55:132-145.
Conference Proceeding: Vision-Based Blind Spot Detection Using Optical Flow.Computer Aided Systems Theory - EUROCAST 2007, 11th International Conference on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 12-16, 2007, Revised Selected Papers; 01/2007
Article: 3D-Visual Detection of Multiple Objects and Structural Features in Complex and Dynamic Indoor Environments[show abstract] [hide abstract]
ABSTRACT: In this paper, it is presented an algorithm for processing visual data to obtain relevant information that will be afterwards used to track the different moving objects in complex indoor environments. In autonomous robots applications, visual detection of the obstacles in a dynamic environment from a mobile platform is a complicated task. The robustness of this process is fundamental in tracking and navigation reliability for autonomous robots. The solution exposed in the document is based on a stereo-vision system; so that 3D information related to each object position in the local environment of the robot is extracted directly form the cameras. In the proposed application, all objects, both dynamic and static, in the local environment of the robot but the structure of the environment itself are considered to be obstacles. With this specification a distinction between building elements (ceiling, walls, columns and so on) and the rest of items in the robot surroundings is needed. Therefore, a classification has to be developed altogether with the detection task. On the other hand, the obtained data can be used to implement a partial reconstruction of the environmental structure that surrounds the robot. All these algorithms explained in detail in the following paragraphs and visual results are also included at the end of the paper.
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ABSTRACT: Different solutions have been proposed for multiple objects tracking based on probabilistic algorithms. In this paper, the authors propose the use of an only particle filter to track a variable number of objects. The robustness and adaptability of the estimator are increased by the use of a clustering algorithm. The measurements used in the tracking process are extracted from a stereovision system, and thus, the 3D position of the tracked objects is obtained at each time step. Tracking results are presented at the end of the paper, showing the reliability of the proposals.