Industrial Robot ( IF 1.8 ) Pub Date : 2020-12-14 , DOI: 10.1108/ir-11-2019-0225 Reyes Rios-Cabrera , Ismael Lopez-Juarez , Alejandro Maldonado-Ramirez , Arturo Alvarez-Hernandez , Alan de Jesus Maldonado-Ramirez
Purpose
This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them.
Design/methodology/approach
The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed.
Findings
The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general.
Research limitations/implications
The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation.
Practical implications
The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks.
Originality/value
The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.
中文翻译:
用于移动服务机器人的3D对象的动态分类
目的
本文旨在提出一种有效地对3D对象模型进行分类的对象检测方法。作者提出了一种动态生成的分层体系结构,以计算非常快的对象的3D姿态,以使移动服务机器人可以掌握它们。
设计/方法/方法
本研究中使用的方法基于动态金字塔搜索和快速模板表示,元数据和无上下文语法。在实验中,作者使用配备RGBD相机的全向KUKA移动机械手来定位人类需要的对象。所提出的体系结构基于有效的对象检测和视觉伺服。在实验中,机器人成功地找到了3D姿势。本提议不限于特定的机器人或物体,并且可以根据需要增长。
发现
作者介绍了使用上下文无关文法和3D对象检测的动态分类,并通过几次实验对概念进行了证明。作者获得了令人鼓舞的结果,表明他们的方法可以扩展到更复杂的场景,并且可以在实际场景中的将来应用中使用,这些场景在诸如服务机器人或一般工业等领域都需要移动机器人。
研究局限/意义
实验是使用移动式KUKA youBot进行的。可伸缩性和更健壮的算法将改进本建议。在第一阶段,作者进行了实验验证。
实际影响
当前的提案描述了一种可伸缩的体系结构,可以在其中添加或重新编程更多的代理以处理更复杂的任务。
创意/价值
这项研究的主要贡献在于用于快速检测3D对象的动态分类方案,以及为检验该方法的可行性而进行的课题和实验。通常,最新技术将类别视为固定类别,并对数据集进行静态查询。在本方法中,没有固定的类别,它们是动态创建和组合的,以加快检测速度。