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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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Nature
2024
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| 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 |
| _version_ | 1811138414932656128 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-09-25T03:49:49Z |
| format | Conference or Workshop Item |
| id | UMPir41388 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:49:49Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | UMPir413882024-05-24T03:45:25Z 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 (Published) https://doi.org/10.1007/978-981-99-8819-8_23 |
| 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 |
| title | 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_short | 3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm |
| title_sort | 3d lidar vehicle perception and classification using 3d machine learning algorithm |
| topic | TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics TS Manufactures |
| 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 |
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