基於深度學習之語義分割用於隨機物件夾取

本篇論文主要提出三點貢獻:(1)由於在訓練深度類神經網路時需要大量的訓練資料，而這些資料往往要由大量的人工與時間手動慢慢標記出來，整個流程曠日廢時，因此本論文設計一個自動產生標記資料的方法，利用GrabCut演算法及K-means演算法來自動標記資料。經由本論文提出的方法，只需要少數原始資料，就可以產生足量的訓練資料，並進行深度網路的訓練進行語義分割達到不錯的效果。(2)本篇論文使用一個基於深度神經網路及點雲影像在複雜場景下進行隨機物件夾取。首先我們使用卷積類神經網路進行語義分割，利用語義分割所得到的結果將目標物的點雲從場景取出來，最後利用FPFH特徵及RANSAC演算法計算夾取姿態。(3) 在運動學奇異點的問題上，本論文使用一個有效率的方法，來進行奇異點的偵測與迴避。在實驗結果中，本論文提出的方法可以成功的辨識物品及估測出夾取姿態，並利用7軸機械手臂進行隨機物件夾取的任務。

關鍵字

深度學習；卷積類神經網路；語義分割；點雲；姿態估測

並列摘要

In this thesis, we have three main contributions as follows. (1) An automatic data generating scheme is proposed to automatically generate training data for training the deep neural network. Training a deep neural network requires a large amount of data, which usually is difficult to be obtained by manual craft and may cost plenty of time and laborious. In our proposed data generation scheme, we use GrabCut algorithm and K-means algorithm to label the image. By using this scheme, we can efficiently produce 7600 labeled data sets when there are only 200 original ones. (2) A deep learning and point cloud based work-flow is proposed to solve the problem of random object picking task in the clutter environment. We firstly build a deep Convolution Neural Networks (CNNs) to compute the pixel-wise probabilities of object in RGB image. Next, we use the pixel-wise probabilities obtained from the CNNs to extract the point cloud of in the scene and use Fast Point Feature Histogram (FPFH) and RANSAC algorithm to compute the 3D grasp pose of object. (3) An efficient method is utilized to detect and avoid the singularity in kinematic. In the end, we can successfully use a 7-degree-of-freedom manipulator to accomplish random object picking tasks in the clutter environment.

並列關鍵字

Deep Learning ； Convolution Neural Networks ； Semantic Segmentation ； Point Cloud ； Pose Estimation

參考文獻

[3] Y.J. Huang, Y.C. Lai, R.J. Chen, C.Y. Tsai, and C.C. Wong, “A Deep Learning-Based Object Detection Algorithm Applied in Shelf-Picking Robot,” International Automatic Control conference (CACS), 2017

[5] S. Ren, K. He, R. Girshick, and J. SunFaster, “R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in neural information processing systems, p.p. 91-99, 2015

[6] R. Jonschkowski, C. Eppner, S. Hofer, R.M. Martin, and O. Brock, “Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge,” IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), p.p. 1-7, 2016

[7] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” arXiv preprint arXiv:1606.00915, 2017

[9] C.H. Wu, S.Y. Jiang, and K.T. Song, “CAD-Based Pose Estimation for Random Bin-Picking of Multiple Object,” International Conference on Control, Automation and Systems(ICCAS), p.p 1645-1649, 2015

國際替代計量

基於深度學習之語義分割用於隨機物件夾取

全文下載

主題瀏覽