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A Study of Effective Recognition of Environment by Integrating Global and Local Visions

整合全球与本土视野对环境的有效认知研究

基本信息

批准号：
06452255
负责人：
YACHIDA Masahiko
金额：
$ 4.22万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-06452255/
关键词：
Mobile Robot Robot Vision Global Vision Local Vision Ominidirectional Image Sensor Environmental Model 画像理解ロボット経路計画位置計測

项目摘要

Last year, we exploited a new omni-directional image sensor in which a hyperboloidal mirror is aligned axis-to-axis over the input-lens of camera. Global models or maps of the environment surrounding a robot were aquired by loading with this sensor, and the planning and the revision of a moving path were succesfully achieved. Our new achievement in this year is as follows.[1] Detection of a Target Object :A method to detect a predefined target object is proposed. For example, the target can be (1) the object to work on, (2) landmarks on the path to a given goal, (3) obstacles which disturb the robot from moving, (4) some suspicious object, or (5) a moving one against which the robot may collide, and so on. In our method, specialized modules are installed, each assigned the task of finding objects in one of these categories. Each module looks around the overall surrounding area, and if detecting any candidate of the target, run the local-vision module to look into its fine feature.[2] Recognition of Fine 3D-structure of a Detected ObjectThe sensor, focusing attention to an object detected by the global vision, recognizes the fine 3D-structure of the object. Since the abstract structure is already known by the global sight, another camera which realizes a local vision is shifted so that the target comes into the center of the image, and then zoomed until the size of the object's image grows as large as the vision-frame. The focus of the lens is also arranged to make the image clear. Based on the input from plural directions, the accurate 3D-structures of target objects were obtained eventually.[3] Experimental Evaluation of the Integral Vision SensorWe developed an image sensor in which the global and the local visions are integrated. Employing this new sensor, objects in real environments suchas the corridor of our university or our laboratory were recognized succesfully.

去年，我们开发了一种新的全向图像传感器，其中双曲面反射镜在相机的输入镜头上轴对轴对齐。通过加载该传感器获得机器人周围环境的全局模型或地图，并成功地实现了移动路径的规划和修正。我们今年的新成就如下目标对象的检测：提出了一种检测预定义目标对象的方法。例如，目标可以是(1)要工作的物体，(2)到达给定目标的路径上的地标，(3)妨碍机器人移动的障碍物，(4)一些可疑的物体，或(5)机器人可能与之相撞的移动物体，等等。在我们的方法中，安装了专门的模块，每个模块都分配了在这些类别中查找对象的任务。每个模块查看整个周围区域，如果检测到任何候选目标，则运行局部视觉模块查看其精细特征识别被检测物体的精细3d结构传感器将注意力集中在全局视觉检测到的物体上，识别物体的精细3d结构。由于抽象结构已经被全局视觉所知道，因此另一个实现局部视觉的相机被移动，使目标进入图像的中心，然后缩放，直到物体图像的大小与视觉框架一样大。镜头的焦点也被安排以使图像清晰。基于多个方向的输入，最终获得目标物体的精确三维结构集成视觉传感器的实验评价我们研制了一种集成全局和局部视觉的图像传感器。利用这种新型传感器，我们成功地识别了真实环境中的物体，如我们大学或我们实验室的走廊。