基于点云数据的服务机器人平面提取

H. Masuta, T. Motoyoshi, K. Koyanagi, K. Sawai, T. Oshima
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引用次数: 1

摘要

介绍了一种基于点云数据的服务机器人感知未知物体的平面提取方法。最近,深度传感器被用于机器人的三维空间感知。深度传感器已被用于识别未知环境,如表面重构、模型拟合等。点云库是处理三维点云数据的典型开源库。然而,机器人抓取感知存在计算成本高、小物体感知精度低等局限性。因此,我们提出了基于pso的平面检测方法,利用RG从检测到的平面组合中重建目标。为了验证未知目标平面检测的精度和计算成本,我们证明了该方法具有更高的精度和更少的计算成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plane extraction using Point Cloud data for service robot
This paper describes an plane extraction method using point cloud data to perceive an unknown object for a service robot. Recently, depth sensors are used to perceive 3D space for a robot. A depth sensor have been used to recognize unknown environment, such as surface reconstruction, model fitting and so on. Point Cloud Library is typical open source library to deal with 3D point cloud data. However, robot perception for grasping have limitations with high computational costs and low-accuracy for perceiving small objects. Therefore, we propose the PSO-based plane detection method with RG to reconstruct an object from a combination of detected planes. To verify accuracy and computational cost for the plane detection of unknown object, we show that the proposed method has higher accuracy and less computational cost for the proposed method.
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