VisGraB: A benchmark for vision-based grasping
VisGraB: A benchmark for vision-based grasping
复制标题
VisGraB：基于视觉的抓取基准
DOI：
10.2478/s13230-012-0020-5
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
N. Krüger
中科院分区：
文献类型：
--
作者：
G. Kootstra;M. Popovic;J. A. Jørgensen;D. Kragic;H. G. Petersen;N. Krüger
我们提出了一个数据库和一个软件工具VisGraB，用于在没有先验对象知识的情况下对基于视觉的未知对象抓取方法进行基准测试。基准测试是真实世界和模拟实验设置的结合。数据库中包含包含多个不同配置对象的真实场景的立体图像。用户需要提供一种基于真实视觉输入的抓取生成方法。然后通过所提供的抓握模拟器计划、执行和评估抓握，其中使用了几种抓握质量测量方法进行评估。这种设置的优点是，在处理真实世界图像中存在的动态、噪声和不确定性时，可以执行和评估大量抓取。VisGraB可以在不同的抓取方法之间进行公平的比较。此外，用户不需要处理机器人的硬件，而是专注于视觉方法。作为基准，我们的把握策略的基准结果包括在内。
We present a database and a software tool, VisGraB, for benchmarking of methods for vision-based grasping of unknown objects with no prior object knowledge. The benchmark is a combined real-world and simulated experimental setup. Stereo images of real scenes containing several objects in different configurations are included in the database. The user needs to provide a method for grasp generation based on the real visual input. The grasps are then planned, executed, and evaluated by the provided grasp simulator where several grasp-quality measures are used for evaluation. This setup has the advantage that a large number of grasps can be executed and evaluated while dealing with dynamics and the noise and uncertainty present in the real world images. VisGraB enables a fair comparison among different grasping methods. The user furthermore does not need to deal with robot hardware, focusing on the vision methods instead. As a baseline, benchmark results of our grasp strategy are included.