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

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 bette...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sumaira Manzoor, Sung-Hyeon Joo, Eun-Jin Kim, Sang-Hyeon Bae, Gun-Gyo In, Jeong-Won Pyo, Tae-Yong Kuc
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	3D visual recognition sensors object detection place recognition camera LiDAR Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/6bdb6e1ff3e244708b4bddff5ffee102
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6bdb6e1ff3e244708b4bddff5ffee102
record_format	dspace
spelling	oai:doaj.org-article:6bdb6e1ff3e244708b4bddff5ffee1022021-11-11T19:07:35Z3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey10.3390/s212171201424-8220https://doaj.org/article/6bdb6e1ff3e244708b4bddff5ffee1022021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7120https://doaj.org/toc/1424-82203D 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.Sumaira ManzoorSung-Hyeon JooEun-Jin KimSang-Hyeon BaeGun-Gyo InJeong-Won PyoTae-Yong KucMDPI AGarticle3D visual recognitionsensorsobject detectionplace recognitioncameraLiDARChemical technologyTP1-1185ENSensors, Vol 21, Iss 7120, p 7120 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	3D visual recognition sensors object detection place recognition camera LiDAR Chemical technology TP1-1185
spellingShingle	3D visual recognition sensors object detection place recognition camera LiDAR Chemical technology TP1-1185 Sumaira Manzoor Sung-Hyeon Joo Eun-Jin Kim Sang-Hyeon Bae Gun-Gyo In Jeong-Won Pyo Tae-Yong Kuc 3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey
description	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.
format	article
author	Sumaira Manzoor Sung-Hyeon Joo Eun-Jin Kim Sang-Hyeon Bae Gun-Gyo In Jeong-Won Pyo Tae-Yong Kuc
author_facet	Sumaira Manzoor Sung-Hyeon Joo Eun-Jin Kim Sang-Hyeon Bae Gun-Gyo In Jeong-Won Pyo Tae-Yong Kuc
author_sort	Sumaira Manzoor
title	3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey
title_short	3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey
title_full	3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey
title_fullStr	3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey
title_full_unstemmed	3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey
title_sort	3d recognition based on sensor modalities for robotic systems: a survey
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6bdb6e1ff3e244708b4bddff5ffee102
work_keys_str_mv	AT sumairamanzoor 3drecognitionbasedonsensormodalitiesforroboticsystemsasurvey AT sunghyeonjoo 3drecognitionbasedonsensormodalitiesforroboticsystemsasurvey AT eunjinkim 3drecognitionbasedonsensormodalitiesforroboticsystemsasurvey AT sanghyeonbae 3drecognitionbasedonsensormodalitiesforroboticsystemsasurvey AT gungyoin 3drecognitionbasedonsensormodalitiesforroboticsystemsasurvey AT jeongwonpyo 3drecognitionbasedonsensormodalitiesforroboticsystemsasurvey AT taeyongkuc 3drecognitionbasedonsensormodalitiesforroboticsystemsasurvey
_version_	1718431588929241088

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

Ejemplares similares