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使用距离图像进行机器人的视觉控制。

Visual control of robots using range images.

机构信息

Physics, Systems Engineering and Signal Theory Department, University of Alicante, PO Box 99, Alicante 03080, Spain.

出版信息

Sensors (Basel). 2010;10(8):7303-22. doi: 10.3390/s100807303. Epub 2010 Aug 4.

DOI:10.3390/s100807303
PMID:22163604
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3231188/
Abstract

In the last years, 3D-vision systems based on the time-of-flight (ToF) principle have gained more importance in order to obtain 3D information from the workspace. In this paper, an analysis of the use of 3D ToF cameras to guide a robot arm is performed. To do so, an adaptive method to simultaneous visual servo control and camera calibration is presented. Using this method a robot arm is guided by using range information obtained from a ToF camera. Furthermore, the self-calibration method obtains the adequate integration time to be used by the range camera in order to precisely determine the depth information.

摘要

在过去几年中，基于飞行时间 (ToF) 原理的 3D 视觉系统在获取工作空间 3D 信息方面变得越来越重要。本文分析了使用 3D ToF 相机来引导机械臂的方法。为此，提出了一种自适应的视觉伺服控制和相机标定方法。使用这种方法，可以通过 ToF 相机获得的距离信息来引导机械臂。此外，自校准方法获得适当的积分时间，以便距离相机精确地确定深度信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/e3755bfb7b40/sensors-10-07303f18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/27494f651d62/sensors-10-07303f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/0f5b88ccc453/sensors-10-07303f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/f87d74192b13/sensors-10-07303f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/8e34c85824d7/sensors-10-07303f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/3bce9bb6876a/sensors-10-07303f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/5ae9b18f7a64/sensors-10-07303f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/4f8bbcf39999/sensors-10-07303f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/f8ab330819ee/sensors-10-07303f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/40baf689bfa4/sensors-10-07303f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/548cfc796d31/sensors-10-07303f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/7f9df066e2d0/sensors-10-07303f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/6394909afd77/sensors-10-07303f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/140be1f28ce0/sensors-10-07303f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/fd027217b479/sensors-10-07303f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/d9c3c88769f1/sensors-10-07303f15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/76c63f9adb62/sensors-10-07303f16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/5376777a49ba/sensors-10-07303f17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/e3755bfb7b40/sensors-10-07303f18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/27494f651d62/sensors-10-07303f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/0f5b88ccc453/sensors-10-07303f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/f87d74192b13/sensors-10-07303f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/8e34c85824d7/sensors-10-07303f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/3bce9bb6876a/sensors-10-07303f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/5ae9b18f7a64/sensors-10-07303f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/4f8bbcf39999/sensors-10-07303f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/f8ab330819ee/sensors-10-07303f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/40baf689bfa4/sensors-10-07303f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/548cfc796d31/sensors-10-07303f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/7f9df066e2d0/sensors-10-07303f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/6394909afd77/sensors-10-07303f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/140be1f28ce0/sensors-10-07303f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/fd027217b479/sensors-10-07303f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/d9c3c88769f1/sensors-10-07303f15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/76c63f9adb62/sensors-10-07303f16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/5376777a49ba/sensors-10-07303f17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a22/3231188/e3755bfb7b40/sensors-10-07303f18.jpg

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Real-time markerless tracking for augmented reality: the virtual visual servoing framework.用于增强现实的实时无标记跟踪：虚拟视觉伺服框架。
IEEE Trans Vis Comput Graph. 2006 Jul-Aug;12(4):615-28. doi: 10.1109/TVCG.2006.78.