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3D Reconstruction of End-Effector in Autonomous Positioning Process Using Depth Imaging Device

机译：深度成像设备在自主定位过程中末端执行器的3D重建

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摘要

Thereal-time calculation of positioning error, error correction, and state analysis has always been a difficult challenge in the process of manipulator autonomous positioning. In order to solve this problem, a simple depth imaging equipment (Kinect) is used and Kalman filtering method based on three-frame subtraction to capture the end-effector motion is proposed in this paper. Moreover, backpropagation (BP) neural network is adopted to recognize the target. At the same time, batch point cloud model is proposed in accordance with depth video stream to calculate the space coordinates of the end-effector and the target. Then, a 3D surface is fitted by using the radial basis function (RBF) and the morphology. The experiments have demonstrated that the end-effector positioning error can be corrected in a short time. The prediction accuracies of both position and velocity have reached 99% and recognition rate of 99.8% has been achieved for cylindrical object. Furthermore, the gradual convergence of the end-effector center (EEC) to the target center (TC) shows that the autonomous positioning is successful. Simultaneously, 3D reconstruction is also completed to analyze the positioning state. Hence, the proposed algorithmin this paper is competent for autonomous positioning of manipulator. The algorithm effectiveness is also validated by 3D reconstruction. The computational ability is increased and system efficiency is greatly improved.
机译：定位误差的实时计算，误差校正和状态分析一直是机械手自主定位过程中的难题。为了解决这个问题，本文使用了一种简单的深度成像设备（Kinect），并提出了一种基于三帧减法的卡尔曼滤波方法来捕获末端执行器运动。此外，采用反向传播（BP）神经网络来识别目标。同时，根据深度视频流提出批处理点云模型，计算出末端执行器和目标的空间坐标。然后，通过使用径向基函数（RBF）和形态来拟合3D表面。实验表明，可以在短时间内纠正末端执行器的定位误差。圆柱物体的位置和速度预测精度均达到99％，识别率达到99.8％。此外，末端执行器中心（EEC）与目标中心（TC）的逐渐收敛表明，自主定位是成功的。同时，还完成了3D重建以分析定位状态。因此，本文提出的算法能够胜任机械手的自主定位。该算法的有效性也通过3D重建得到了验证。计算能力提高，系统效率大大提高。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第9期|8972764.1-8972764.16|共16页
  • 作者

    Hu Yanzhu; Li Leiyuan;

  • 作者单位

    Beijing Univ Posts & Telecommun, Coll Automat, Beijing 100876, Peoples R China;

    Beijing Univ Posts & Telecommun, Coll Automat, Beijing 100876, Peoples R China;

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