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We describe a novel two stage approach to object localization and tracking using a network of wireless cameras and a mobile robot. In the first stage, a robot travels through the camera network while updating its position in a global coordinate frame which it broadcasts to the cameras. The cameras use this information, along with image plane location of the robot, to compute a mapping from their image...
A new method for efficiently mapping three dimensional environments from a platform carrying a single calibrated camera, and simultaneously localizing the platform within this map is presented in this paper. This is the Monocular SLAM problem in robotics, which is equivalent to the problem of extracting Structure from Motion (SFM) in computer vision. A novel formulation of Monocular SLAM which exploits...
This paper addresses the problem of mapping three dimensional environments from a sequence of images taken by a calibrated camera, and simultaneously generating the camera motion trajectory. This is the Monocular SLAM problem in robotics, and is akin to the Structure from Motion (SFM) problem in computer vision. We present a novel map-aided 6-DOF relative pose estimation method based on a new formulation...
Recently, there has been a marked increase in using machine learning techniques for object detection because of its immunity to noise and variations in backgrounds. SLAM (Simultaneous Localization and Mapping) is an approach to mapping the environment in which the robot moves, by using landmarks, much like the human visual system. The application of a robust object detection system can be extended...
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