CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Thesis Advisor王硕 ; 蔡莹皓
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline控制理论与控制工程
Keyword位姿估计 自编码器 表示学习 度量学习 机器人抓取

    1. 针对位姿估计算法依赖位姿标注的问题,给出了一种基于降噪自编码器的无监督旋转表征学习方法,并在随机二维图形图像数据集上进行仿真实验。实验结果显示,无需位姿标注,降噪自编码器可学习到椭圆、心形、正方形、长方形的旋转表征,并在长方形图形上具有类级别旋转表征提取的能力。
    2. 针对实例级别方法未考虑同类物体同质特征的问题,给出利用降噪自编码器在三维场景物体实例级别以及类级别上进行旋转表征学习的方法。该方法仅需单个物体三维模型进行类级别旋转表征学习。实验结果表明利用该类级别方法可有效学习同类物体不同实例的旋转表征,获得了较好的位姿估计结果。此外,真实环境中的机器人抓取实验验证了类级别位姿估计方法的有效性。
    3.    针对降噪自编码器未考虑不同旋转表征之间位姿约束关系的问题,给出了将度量学习约束应用于降噪自编码器瓶颈层旋转表征的方法。实验结果表明,通过对降噪自编码器瓶颈层旋转表征结合欧式距离损失函数的度量学习约束,可提升物体位姿估计准确率。

Other Abstract

    Object pose estimation is an important research field in the field of intelligent robots. Object pose estimation is defined as, how to obtain an object's 3D translations and 3D rotations under the camera coordinate system, from color or depth images. Object pose estimation is crucial on robot grasping. However, there are theoretical and practical issues remain unsolved such as the influence of illumination variance, noise from sensors, cluttered background and occlusion. These issues will cut down the accuracy of pose estimation. Besides, the dataset of object pose estimation needs excruciating efforts to acquire, since each object needs pose labelling. Furthermore, the homogeneous features, such as shape, are not considered within the same category of objects. The constraints on embedding space are not considered for the orientation representation learning method based on unsupervised learning. Therefore, this thesis focuses on the research of pose estimation of objects to grasp, and provides technical solutions for improving the robot’s grasping skills.

    For the problems mentioned above, the researches and experiments are conducted. Main work is summarized below:
    1. For the problem of training dependence on pose-annotated datasets, unsupervised orientation representation learning based on denoising autoencoder is proposed and experimented on the 2D shapes with random scales and in-plane translations. With our elaborated training pipeline, the autoencoder has learnt the orientations of various shapes without pose annotations. Thus, the proposed method has the ability of categorylevel orientation representation learning.
    2. For the problem without considering the homogeneous feature of the same category of 3D objects, denoising autoencoder is used to learn orientation representation. Only one object 3D model is used as category representative to learn category-level orientation representation. The results show that denoising autoencoder is effective for orientation representation learning both at instance and category level. Our robot grasping experiments demonstrated the effectiveness of the category-level pose estimation method.
    3. For the problem without considering the relations between training samples, deep metric learning method is introduced. Metric learning constraints are applied to the bottleneck layer of denoising autoencoder. The results show that applying Euclidean metric loss to the bottleneck layer of denoising autoencoder could improve the accuracy of pose estimation.

Funding ProjectNational Natural Science Foundation of China[61773378] ; National Natural Science Foundation of China[61773378]
Document Type学位论文
Recommended Citation
GB/T 7714
李晓灿. 机器人抓取目标的表征学习与位姿估计[D]. 中国科学院自动化研究所. 中国科学院大学,2020.
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