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

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

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	3D machine learning; 3D point cloud; Autonomous vehicle; LiDAR
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics T Technology > TS Manufactures
Faculty/Division:	Institute of Postgraduate Studies Centre of Excellence: Automotive Engineering Centre Centre of Excellence: Automotive Engineering Centre Faculty of Manufacturing and Mechatronic Engineering Technology Faculty of Mechanical and Automotive Engineering Technology
Depositing User:	Miss Amelia Binti Hasan
Date Deposited:	24 May 2024 03:45
Last Modified:	24 May 2024 03:45
URI:	http://umpir.ump.edu.my/id/eprint/41388
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