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首页> 外文会议>2014 13th International Conference on Control Automation Robotics amp; Vision >Evaluation of Kinect vision sensor for bin-picking applications: Improved component separation accuracy with combined use of depth map and color image
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Evaluation of Kinect vision sensor for bin-picking applications: Improved component separation accuracy with combined use of depth map and color image

机译：评估Kinect视觉传感器用于分拣应用：结合使用深度图和彩色图像，提高了组件分离的准确性

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摘要

This report describes a problem involved with use of Kinect depth maps for robot picking of randomly stacked components, and also a solution to this problems. When Kinect is installed above stacked parts and processing is performed using only the obtained Kinect depth map information, there are cases when individual small metal components cannot be separately identified. So that the robot can reliably pick up a single component in these cases, this report demonstrates that in areas where the system incorrectly identifies multiple components as a single component, the addition of color image information and blob analysis of the color image results in accurate separation of the individual components, allowing a single item to be identified for picking.
机译：该报告描述了使用Kinect深度图进行机器人随机拾取堆叠组件时涉及的问题，以及该问题的解决方案。将Kinect安装在堆叠部件上方并仅使用获得的Kinect深度图信息进行处理时，有时无法单独识别单个小金属部件。为了使机器人能够在这些情况下可靠地拾取单个组件，该报告表明，在系统错误地将多个组件标识为单个组件的区域中，添加彩色图像信息和彩色图像的斑点分析可实现准确的分离单个组件的组合，从而可以识别单个项目进行拣配。

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