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A Cognitive Approach to Vision for a Mobile Robot

机译:移动机器人视觉的认知方法

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We describe a cognitive vision system for a mobile robot. This system works in a manner similar to the human vision system, using saccadic, vergence and pursuit movements to extract information from visual input. At each fixation, the system builds a 3D model of a small region, combining information about distance, shape, texture and motion. These 3D models are embedded within an overall 3D model of the robot's environment. This approach turns the computer vision problem into a search problem, with the goal of constructing a physically realistic model of the entire environment. At each step, the vision system selects a point in the visual input to focus on. The distance, shape, texture and motion information are computed in a small region and used to build a mesh in a 3D virtual world. Background knowledge is used to extend this structure as appropriate, e.g. if a patch of wall is seen, it is hypothesized to be part of a large wall and the entire wall is created in the virtual world, or if part of an object is recognized, the whole object's mesh is retrieved from the library of objects and placed into the virtual world. The difference between the input from the real camera and from the virtual camera is compared using local Gaussians, creating an error mask that indicates the main differences between them. This is then used to select the next points to focus on. This approach permits us to use very expensive algorithms on small localities, thus generating very accurate models. It also is task-oriented, permitting the robot to use its knowledge about its task and goals to decide which parts of the environment need to be examined. The software components of this architecture include PhysX for the 3D virtual world, OpenCV and the Point Cloud Library for visual processing, and the Soar cognitive architecture, which controls the perceptual processing and robot planning. The hardware is a custom-built pan-tilt stereo color camera. We describe experiments using both static and moving objects.
机译:我们描述了一种用于移动机器人的认知视觉系统。该系统以类似于人类视觉系统的方式工作,使用跳动,发散和追赶运动从视觉输入中提取信息。在每次注视时,系统都会构建一个小区域的3D模型,并结合有关距离,形状,纹理和运动的信息。这些3D模型被嵌入到机器人环境的整体3D模型中。这种方法将计算机视觉问题转变为搜索问题,目的是构建整个环境的物理逼真的模型。在每个步骤中,视觉系统都会在视觉输入中选择一个要聚焦的点。距离,形状,纹理和运动信息在一个较小的区域中进行计算,并用于在3D虚拟世界中构建网格。背景知识被用于适当地扩展该结构,例如。如果看到一堵墙,则假定它是一堵大墙的一部分,并且整个墙都是在虚拟世界中创建的,或者如果识别出一部分对象,则从对象库中检索整个对象的网格,然后置于虚拟世界中。使用本地高斯比较来自真实摄像机和虚拟摄像机的输入之间的差异,从而创建一个错误掩码,指示两者之间的主要差异。然后将其用于选择下一个要关注的点。这种方法使我们可以在很小的地方使用非常昂贵的算法,从而生成非常准确的模型。它也是面向任务的,允许机器人利用其关于任务和目标的知识来决定需要检查环境的哪些部分。该架构的软件组件包括用于3D虚拟世界的PhysX,用于视觉处理的OpenCV和点云库,以及用于控制感知处理和机器人计划的Soar认知架构。硬件是定制的云台立体彩色摄像机。我们描述了同时使用静态对象和移动对象的实验。

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