Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving

Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generally used as vehicle sensors, each of which has its own characteristics. As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception...
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Autor*in:	Dongwan Kang [verfasserIn] Anthony Wong [verfasserIn] Banghyon Lee [verfasserIn] Jungha Kim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	semantic segmentation lidar autonomous vehicle classification neural network deep learning

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 10(2021), 16, p 1960
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:16, p 1960

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics10161960

Katalog-ID:	DOAJ013344366

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allfieldsGer	10.3390/electronics10161960 doi (DE-627)DOAJ013344366 (DE-599)DOAJd791008474394b2fbeb457415881e077 DE-627 ger DE-627 rakwb eng TK7800-8360 Dongwan Kang verfasserin aut Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generally used as vehicle sensors, each of which has its own characteristics. As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception, and LiDAR is used for accurate distance recognition. The ability of a camera to understand a scene has overwhelmingly increased with the recent development of deep learning. In addition, technologies that emulate other sensors using a single sensor are being developed. Therefore, in this study, a LiDAR data-based scene understanding method was developed through deep learning. The approaches to accessing LiDAR data through deep learning are mainly divided into point, projection, and voxel methods. The purpose of this study is to apply a projection method to secure a real-time performance. The convolutional neural network method used by a conventional camera can be easily applied to the projection method. In addition, an adaptive break point detector method used for conventional 2D LiDAR information is utilized to solve the misclassification caused by the conversion from 2D into 3D. The results of this study are evaluated through a comparison with other technologies. semantic segmentation lidar autonomous vehicle classification neural network deep learning Electronics Anthony Wong verfasserin aut Banghyon Lee verfasserin aut Jungha Kim verfasserin aut In Electronics MDPI AG, 2013 10(2021), 16, p 1960 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:16, p 1960 https://doi.org/10.3390/electronics10161960 kostenfrei https://doaj.org/article/d791008474394b2fbeb457415881e077 kostenfrei https://www.mdpi.com/2079-9292/10/16/1960 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 16, p 1960
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language	English
source	In Electronics 10(2021), 16, p 1960 volume:10 year:2021 number:16, p 1960
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authorswithroles_txt_mv	Dongwan Kang @@aut@@ Anthony Wong @@aut@@ Banghyon Lee @@aut@@ Jungha Kim @@aut@@
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callnumber-first	T - Technology
author	Dongwan Kang
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topic_title	TK7800-8360 Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving semantic segmentation lidar autonomous vehicle classification neural network deep learning
topic	misc TK7800-8360 misc semantic segmentation misc lidar misc autonomous vehicle misc classification misc neural network misc deep learning misc Electronics
topic_unstemmed	misc TK7800-8360 misc semantic segmentation misc lidar misc autonomous vehicle misc classification misc neural network misc deep learning misc Electronics
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title	Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving
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title_auth	Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving
abstract	Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generally used as vehicle sensors, each of which has its own characteristics. As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception, and LiDAR is used for accurate distance recognition. The ability of a camera to understand a scene has overwhelmingly increased with the recent development of deep learning. In addition, technologies that emulate other sensors using a single sensor are being developed. Therefore, in this study, a LiDAR data-based scene understanding method was developed through deep learning. The approaches to accessing LiDAR data through deep learning are mainly divided into point, projection, and voxel methods. The purpose of this study is to apply a projection method to secure a real-time performance. The convolutional neural network method used by a conventional camera can be easily applied to the projection method. In addition, an adaptive break point detector method used for conventional 2D LiDAR information is utilized to solve the misclassification caused by the conversion from 2D into 3D. The results of this study are evaluated through a comparison with other technologies.
abstractGer	Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generally used as vehicle sensors, each of which has its own characteristics. As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception, and LiDAR is used for accurate distance recognition. The ability of a camera to understand a scene has overwhelmingly increased with the recent development of deep learning. In addition, technologies that emulate other sensors using a single sensor are being developed. Therefore, in this study, a LiDAR data-based scene understanding method was developed through deep learning. The approaches to accessing LiDAR data through deep learning are mainly divided into point, projection, and voxel methods. The purpose of this study is to apply a projection method to secure a real-time performance. The convolutional neural network method used by a conventional camera can be easily applied to the projection method. In addition, an adaptive break point detector method used for conventional 2D LiDAR information is utilized to solve the misclassification caused by the conversion from 2D into 3D. The results of this study are evaluated through a comparison with other technologies.
abstract_unstemmed	Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generally used as vehicle sensors, each of which has its own characteristics. As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception, and LiDAR is used for accurate distance recognition. The ability of a camera to understand a scene has overwhelmingly increased with the recent development of deep learning. In addition, technologies that emulate other sensors using a single sensor are being developed. Therefore, in this study, a LiDAR data-based scene understanding method was developed through deep learning. The approaches to accessing LiDAR data through deep learning are mainly divided into point, projection, and voxel methods. The purpose of this study is to apply a projection method to secure a real-time performance. The convolutional neural network method used by a conventional camera can be easily applied to the projection method. In addition, an adaptive break point detector method used for conventional 2D LiDAR information is utilized to solve the misclassification caused by the conversion from 2D into 3D. The results of this study are evaluated through a comparison with other technologies.
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Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving

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