Real-Time Grasp Detection Using Improved FMM and Cascaded Neural Networks

L. Weiwei, Wu Peng, Dong Shiwen
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引用次数: 0

Abstract

The successful grasping task for the robotic arm requires precise grasping posture. In this paper, we use the cascading depth network to predict the optimal object grasping pose. The model is mainly divided into two steps: i) generating a set of candidates that contain the regions of objects; ii) getting the optimal capture position by detecting the candidate region, and combining the depth image to obtain the three-dimensional coordinates of the capture position for objects. Due to flaws and edge noise in the depth image of Kinect, an improved FMM (Fast Marching Method) algorithm is used to repair the depth image hole, and then the joint bilateral filtering algorithm is employed to recover the edge noise of the depth image. Experimental results in public dataset and real scenes have demonstrated the effectiveness of the proposed method.
基于改进FMM和级联神经网络的实时抓握检测
机械臂的成功抓取需要精确的抓取姿态。在本文中,我们使用级联深度网络来预测最优目标抓取姿态。该模型主要分为两个步骤:1)生成一组包含目标区域的候选对象;Ii)通过检测候选区域得到最优捕获位置,并结合深度图像得到目标捕获位置的三维坐标。针对Kinect深度图像中存在的缺陷和边缘噪声,采用改进的FMM (Fast Marching Method)算法对深度图像的孔洞进行修复,然后采用联合双边滤波算法对深度图像的边缘噪声进行恢复。公共数据集和真实场景的实验结果证明了该方法的有效性。
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