3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey

Sumaira Manzoor, Sung-Hyeon Joo, Eun-Jin Kim, Sang-Hyeon Bae, Gun-Gyo In, Jeong-Won Pyo, Tae-Yong Kuc
2021 Sensors  
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
more » ... 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.
doi:10.3390/s21217120 pmid:34770429 pmcid:PMC8587961 fatcat:qqoqlkaycbc4xk24j7j2nzqabe