基于粒子群优化的机器人感知三维平面检测

H. Masuta, Shinichiro Makino, Hun-ok Lim
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引用次数: 11

摘要

本文介绍了一种利用三维距离传感器实现智能机器人感知未知物体的三维平面检测方法。在此之前,人们提出了各种方法来感知未知环境。然而,以往的未知目标检测方法存在计算成本高、小目标检测精度低等问题。为了解决上述问题,我们提出了一种基于三维平面检测的在线可处理未知目标检测方法。该方法由基于区域生长的粒子群优化(PSO)的简单平面检测和综合目标平面检测组成。简单平面检测的重点是小平面检测和降低计算成本。为了提高精度,我们采用了PSO和RG。综合目标平面检测注重检测平面的稳定性。实验结果表明,该算法降低了计算成本,能够实时计算机器人的操作。并且,该方法可以检测到特定物体的小平面。此外,我们还讨论了该方法在降低计算成本和提高平面检测精度方面的协调能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3D plane detection for robot perception applying particle swarm optimization
This article describes a 3D plane detection method for an intelligent robot to perceive an unknown object with 3D range sensor. Previously, various method has been proposed to perceive unknown environment. However, the previous unknown object detection method has problems which are high computational costs and low-accuracy for small object. In order to solve the mentioned problems, we have proposed an online processable unknown object detection based on a 3D plane detection method. The proposed method consists of simple plane detection applying particle swarm optimization (PSO) with region growing (RG) and integrated object plane detection. The simple plane detection is focused on small plane detection and reducing computational costs. To improve the accuracy, we apply PSO and RG. And, integrated object plane detection focuses on stability of detecting plane. As experimental results, we show that the computational cost is reduced to be able to calculate in real time for robot operation. And, the proposed method detects small planes of specific objects. Furthermore, we discuss the capability of proposed method which coordinate the ability of reducing computational costs and improving the plane detection accuracy.
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