Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea.
Sensors (Basel). 2021 Oct 27;21(21):7120. doi: 10.3390/s21217120.
3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human-robot interaction. Autonomous robots equipped with 3D recognition capability can better perform their social roles through supportive task assistance in professional jobs and effective domestic services. For active assistance, social robots must recognize their surroundings, including objects and places to perform the task more efficiently. This article first highlights the value-centric role of social robots in society by presenting recently developed robots and describes their main features. Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models. In this direction, we delineate the research gaps that need to be addressed, summarize 3D recognition datasets, and present performance comparisons. Finally, a discussion of future research directions concludes the article. This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves as a guide to those who are interested in vision-based robotics applications.
3D 视觉识别是大多数在现实世界中运行的自主机器人系统的先决条件。它使机器人能够执行各种任务，例如跟踪、理解环境和人机交互。配备 3D 识别能力的自主机器人可以通过在专业工作和有效的家庭服务中提供支持性任务协助，更好地发挥其社会作用。为了主动协助，社交机器人必须识别周围环境，包括物体和地点，以更有效地执行任务。本文首先通过介绍最近开发的机器人，强调了社会机器人在社会中的以价值为中心的作用，并描述了它们的主要特点。受社交机器人识别能力的启发，我们提出了基于深度学习模型的传感器模态 3D 物体和位置识别的数据表示方法分析。在这个方向上，我们描绘了需要解决的研究差距，总结了 3D 识别数据集，并给出了性能比较。最后，讨论了未来的研究方向，结束了本文。本调查旨在展示基于传感器模态的基于深度学习的方法的最新 3D 视觉识别发展如何为进一步研究奠定基础，并为对基于视觉的机器人应用感兴趣的人提供指导。
