Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Diagnosis of COVID-19 on Chest X-ray (CXR) Images Using CNN with Transfer Learning and Integrated Stacking Ensemble Learning Read chapter Read first chapter

2024 | OriginalPaper | Chapter

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

Authors : Ericsson Yong, Muhammad Aizzat Zakaria, Mohamad Heerwan Peeie, M. Izhar Ishak

Published in: Intelligent Manufacturing and Mechatronics

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Ride Comfort Assessment of a Sitting Pregnant Women During Cornering: Autonomous Vehicle Simulation Maneuvering Analysis
next chapter PID Controller Optimized by Bird Mating Optimizer for Vibration Control of Horizontal Flexible Plate
Literature
1.
Cheng H (2011) Autonomous intelligent vehicles: theory, algorithms, and implementation. Springer Science & Business Media
2.
Zamanakos G et al (2021) A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving. Comput Graph 99:153–181CrossRef
3.
Qi et al (2017) PointNet++: deep hierarchical feature learning on point sets in a metric space
4.
Zhou Y, Tuzel O (2018) VoxelNet: end-to-end learning for point cloud based 3D object detection. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 4490–4499
5.
Lang et al (2019) Pointpillars: fast encoders for object detection from point clouds. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 12689–12697
6.
Beltrán J, Guindel C, Moreno FM, Cruzado D, Garcia F, De La Escalera A (2018) Birdnet: a 3d object detection framework from lidar information. In: 2018 21st international conference on intelligent transportation systems (ITSC), IEEE, pp 3517–3523
7.
Zarzar J, Giancola S, Ghanem B (2019) PointRGCN: graph convolution networks for 3D vehicles detection refinement
8.
Yan Y et al (2018) Second: sparsely embedded convolutional detection. Sensors 18(10):7–8CrossRef
9.
Yang Z et al (2019) STD: sparse-to-dense 3D object detector for point cloud. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp 1951–1960
10.
Li J et al (2020) 3D IoU-Net: IoU guided 3D object detector for point clouds. ArXiv, abs/2004.04962
11.
Shi S, Wang X, Li H (2018) PointRCNN: 3D object proposal generation and detection from point cloud
12.
Yang Z et al (2020) 3dssd: point-based 3d single stage object detector. In: 3DSSD: point-based 3D single stage object detector, pp 11040–11048
13.
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks
14.
Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition, pp 3354–3361
15.
Guo Y et al (2021) Deep learning for 3D point clouds: a survey. IEEE Trans Pattern Anal Mach Intell 43(12):4338–4364CrossRef
16.
Li Y et al (2021) Deep learning for LiDAR point clouds in autonomous driving: a review. IEEE Trans Neural Netw Learn Syst 32(8):3412–3432CrossRef
17.
Yuwono YD (n.d.) Comparison of 3D object detection methods for people detection in underground mine
18.
Metadata
Title
3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
Authors
Ericsson Yong
Muhammad Aizzat Zakaria
Mohamad Heerwan Peeie
M. Izhar Ishak
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Intelligent Manufacturing and Mechatronics

Print ISBN: 978-981-9988-18-1

Electronic ISBN: 978-981-9988-19-8

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-99-8819-8_23

Premium Partners

    Image Credits