Daniel Langdon

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In this paper, we tackle the problem of unsupervised selection and posterior recognition of visual landmarks in images sequences acquired by an indoor mobile robot. This is a highly valuable perceptual capability for a wide variety of robotic applications, in particular autonomous navigation. Our method combines a bottom-up data driven approach with(More)
Detection of visual landmarks is an important problem in the development of automated, vision-based agents working on unstruc-tured environments. In this paper, we present an unsupervised approach to select and to detect landmarks in images coming from a video stream. Our approach integrates three main visual mechanisms: attention, area segmentation, and(More)
This paper describes the main activities and achievements of our research group on Machine Intelligence and Robotics (Grima) at the Computer Science Department , Pontificia Universidad Catolica de Chile (PUC). Since 2002, we have been developing an active research in the area of indoor autonomous social robots. Our main focus has been the cognitive side of(More)
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