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3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm

3D LiDAR-based object detection during autonomous vehicle navigation is a trending field in autonomous vehicle research and development. As 3D LiDAR is resistant to light interference while capable of capturing detailed 3D spatial structures of the detected objects, it is the main perception sensor...

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Main Authors:	Yong, Ericsson, Muhammad Aizzat, Zakaria, Mohamad Heerwan, Peeie, M. Izhar, Ishak
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer Nature 2024
Subjects:	TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/41388/1/3D%20LiDAR%20Vehicle%20Perception%20and%20Classification%20Using%203D%20Machine.pdf http://umpir.ump.edu.my/id/eprint/41388/2/3D%20LiDAR%20Vehicle%20Perception%20and%20Classification%20Using%203D%20Machine%20Learning%20Algorithm.pdf http://umpir.ump.edu.my/id/eprint/41388/ https://doi.org/10.1007/978-981-99-8819-8_23
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spelling	my.ump.umpir.413882024-05-24T03:45:25Z http://umpir.ump.edu.my/id/eprint/41388/ 3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm Yong, Ericsson Muhammad Aizzat, Zakaria Mohamad Heerwan, Peeie M. Izhar, Ishak TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics TS Manufactures 3D LiDAR-based object detection during autonomous vehicle navigation is a trending field in autonomous vehicle research and development. As 3D LiDAR is resistant to light interference while capable of capturing detailed 3D spatial structures of the detected objects, it is the main perception sensor for autonomous vehicles. With its improved accessibility in the recent years, the advent of deep learning had allowed feature learning from sparse 3D point clouds. Hence, this leads a plethora of methods in object detection for 3D sparse point clouds. In this research, an extensive experiment was conducted using various 3D LiDAR object detections for various forms like pillar-form, point-form and voxel-form onto multiple point cloud data sets captured using Robotic Operating System (ROS). Based on experiments conducted, pillar-form point cloud data is suitable for dense point clouds, while voxel-form is optimal for both indoors and outdoors environment. Springer Nature 2024 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41388/1/3D%20LiDAR%20Vehicle%20Perception%20and%20Classification%20Using%203D%20Machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/41388/2/3D%20LiDAR%20Vehicle%20Perception%20and%20Classification%20Using%203D%20Machine%20Learning%20Algorithm.pdf Yong, Ericsson and Muhammad Aizzat, Zakaria and Mohamad Heerwan, Peeie and M. Izhar, Ishak (2024) 3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm. In: Intelligent Manufacturing and Mechatronics, Lecture Notes in Networks and Systems. 4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023 , 07 – 08 August 2023 , Pekan, Malaysia. pp. 291-302., 850. ISSN 2367-3389 ISBN 978-981-99-8819-8 https://doi.org/10.1007/978-981-99-8819-8_23
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics TS Manufactures
spellingShingle	TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics TS Manufactures Yong, Ericsson Muhammad Aizzat, Zakaria Mohamad Heerwan, Peeie M. Izhar, Ishak 3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
description	3D LiDAR-based object detection during autonomous vehicle navigation is a trending field in autonomous vehicle research and development. As 3D LiDAR is resistant to light interference while capable of capturing detailed 3D spatial structures of the detected objects, it is the main perception sensor for autonomous vehicles. With its improved accessibility in the recent years, the advent of deep learning had allowed feature learning from sparse 3D point clouds. Hence, this leads a plethora of methods in object detection for 3D sparse point clouds. In this research, an extensive experiment was conducted using various 3D LiDAR object detections for various forms like pillar-form, point-form and voxel-form onto multiple point cloud data sets captured using Robotic Operating System (ROS). Based on experiments conducted, pillar-form point cloud data is suitable for dense point clouds, while voxel-form is optimal for both indoors and outdoors environment.
format	Conference or Workshop Item
author	Yong, Ericsson Muhammad Aizzat, Zakaria Mohamad Heerwan, Peeie M. Izhar, Ishak
author_facet	Yong, Ericsson Muhammad Aizzat, Zakaria Mohamad Heerwan, Peeie M. Izhar, Ishak
author_sort	Yong, Ericsson
title	3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
title_short	3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
title_full	3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
title_fullStr	3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
title_full_unstemmed	3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
title_sort	3d lidar vehicle perception and classification using 3d machine learning algorithm
publisher	Springer Nature
publishDate	2024
url	http://umpir.ump.edu.my/id/eprint/41388/1/3D%20LiDAR%20Vehicle%20Perception%20and%20Classification%20Using%203D%20Machine.pdf http://umpir.ump.edu.my/id/eprint/41388/2/3D%20LiDAR%20Vehicle%20Perception%20and%20Classification%20Using%203D%20Machine%20Learning%20Algorithm.pdf http://umpir.ump.edu.my/id/eprint/41388/ https://doi.org/10.1007/978-981-99-8819-8_23
_version_	1822924350297210880
score	13.232414

3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm

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