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Modeling the environment with egocentric vision systems

机译:使用以自我为中心的视觉系统对环境进行建模

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More and more intelligent systems, such as robots or wearable systems, are present in our everyday life.?This kind of systems interact with the environment so they need suitable models of their surrounding.?Depending on the tasks that they have to perform, the information required in those models changes: from highly detailed 3D models for autonomous navigation systems, to semantic models including important?information for the user. These models are created using the sensory data provided by the system (Fig. 1).?Cameras are an important sensor included in most intelligent systems thanks to their small size, cheap?prices and the great amount of information that they provide. This thesis studies and develops new?methods to create models of the environment with different levels of precision and semantic information.?There are two coomon key-points in the subsequent presented approaches: - The use of egocentric vision systems. All the vision systems and image sequences used in this?thesis characterize for a first-person (egocentric) point of view. - The use of omnidirectional vision. This kind of vision systems provide much more information?that conventional cameras thanks to their wide field of view. This thesis studies how computer vision can be used to create different models of the environment. To?test our proposals, different cameras have been used, both in robotic and wearable platforms.
机译:我们的日常生活中出现了越来越多的智能系统,例如机器人或可穿戴系统。这类系统与环境相互作用,因此需要合适的周围环境模型。根据它们必须执行的任务,这些模型中所需的信息发生了变化:从用于自主导航系统的高度详细的3D模型,到包含针对用户的重要信息的语义模型。这些模型是使用系统提供的感官数据创建的(图1)。相机是大多数智能系统中包含的重要传感器,这归因于它们的体积小,价格便宜以及它们提供的大量信息。本文研究和开发了新的方法,以创建具有不同级别的精度和语义信息的环境模型。在随后提出的方法中,有两个共同点:-以自我为中心的视觉系统的使用。本文中使用的所有视觉系统和图像序列都具有第一人称(以自我为中心)的观点。 -使用全向视觉。这种视觉系统可提供比传统相机更多的信息,这要归功于它们的广阔视野。本文研究如何使用计算机视觉来创建不同的环境模型。为了测试我们的建议,已在机器人和可穿戴平台中使用了不同的相机。

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