Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors

Yang Zhongfan; Wang Xiaogang; Hou Jing

doi:doi:10.11648/j.ijssn.20221001.11

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Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors

Yang Zhongfan, Wang Xiaogang, Hou Jing

Published in International Journal of Sensors and Sensor Networks (Volume 10, Issue 1)

Received: 22 December 2021 Accepted: 7 January 2022 Published: 15 January 2022

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Abstract

As a consumer-grade portable depth image data acquisition device, the depth camera is widely used in the field of computer vision, such as slam, autonomous driving, environment perception, etc. However, due to the limitation of the device angle, the complete 3D point cloud of the target cannot be obtained at one time. Point cloud registration can complete the overlap of two frames of point clouds. Therefore, a multi-frame point cloud fusion method based on key points and registration is proposed. First, the point cloud is calculated on the depth map obtained by the depth camera, and then an improved point cloud filtering algorithm based on the normal vector inner cumulus is used to remove the background and noise points. Secondly, four key point detection algorithms and three registration algorithms with different principles are applied to the point cloud data obtained by the depth camera, and the applicable scenarios and limitations of each algorithm are analyzed. Finally, a multi-frame point cloud fusion algorithm is used to splice the point clouds, and the redundant points after splicing are filtered out to obtain a complete point cloud of the object. The experimental verification of the target object using the depth camera shows that the proposed method can obtain the complete point cloud data of the target object robustly.

Published in	International Journal of Sensors and Sensor Networks (Volume 10, Issue 1)
DOI	10.11648/j.ijssn.20221001.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Point Cloud, Kinect, Filter, Registration

References

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APA Style

Yang Zhongfan, Wang Xiaogang, Hou Jing. (2022). Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors. International Journal of Sensors and Sensor Networks, 10(1), 1-6. https://doi.org/10.11648/j.ijssn.20221001.11

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ACS Style

Yang Zhongfan; Wang Xiaogang; Hou Jing. Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors. Int. J. Sens. Sens. Netw. 2022, 10(1), 1-6. doi: 10.11648/j.ijssn.20221001.11

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AMA Style

Yang Zhongfan, Wang Xiaogang, Hou Jing. Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors. Int J Sens Sens Netw. 2022;10(1):1-6. doi: 10.11648/j.ijssn.20221001.11

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@article{10.11648/j.ijssn.20221001.11,
  author = {Yang Zhongfan and Wang Xiaogang and Hou Jing},
  title = {Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {10},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijssn.20221001.11},
  url = {https://doi.org/10.11648/j.ijssn.20221001.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20221001.11},
  abstract = {As a consumer-grade portable depth image data acquisition device, the depth camera is widely used in the field of computer vision, such as slam, autonomous driving, environment perception, etc. However, due to the limitation of the device angle, the complete 3D point cloud of the target cannot be obtained at one time. Point cloud registration can complete the overlap of two frames of point clouds. Therefore, a multi-frame point cloud fusion method based on key points and registration is proposed. First, the point cloud is calculated on the depth map obtained by the depth camera, and then an improved point cloud filtering algorithm based on the normal vector inner cumulus is used to remove the background and noise points. Secondly, four key point detection algorithms and three registration algorithms with different principles are applied to the point cloud data obtained by the depth camera, and the applicable scenarios and limitations of each algorithm are analyzed. Finally, a multi-frame point cloud fusion algorithm is used to splice the point clouds, and the redundant points after splicing are filtered out to obtain a complete point cloud of the object. The experimental verification of the target object using the depth camera shows that the proposed method can obtain the complete point cloud data of the target object robustly.},
 year = {2022}
}

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TY  - JOUR
T1  - Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors
AU  - Yang Zhongfan
AU  - Wang Xiaogang
AU  - Hou Jing
Y1  - 2022/01/15
PY  - 2022
N1  - https://doi.org/10.11648/j.ijssn.20221001.11
DO  - 10.11648/j.ijssn.20221001.11
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2329-1788
UR  - https://doi.org/10.11648/j.ijssn.20221001.11
AB  - As a consumer-grade portable depth image data acquisition device, the depth camera is widely used in the field of computer vision, such as slam, autonomous driving, environment perception, etc. However, due to the limitation of the device angle, the complete 3D point cloud of the target cannot be obtained at one time. Point cloud registration can complete the overlap of two frames of point clouds. Therefore, a multi-frame point cloud fusion method based on key points and registration is proposed. First, the point cloud is calculated on the depth map obtained by the depth camera, and then an improved point cloud filtering algorithm based on the normal vector inner cumulus is used to remove the background and noise points. Secondly, four key point detection algorithms and three registration algorithms with different principles are applied to the point cloud data obtained by the depth camera, and the applicable scenarios and limitations of each algorithm are analyzed. Finally, a multi-frame point cloud fusion algorithm is used to splice the point clouds, and the redundant points after splicing are filtered out to obtain a complete point cloud of the object. The experimental verification of the target object using the depth camera shows that the proposed method can obtain the complete point cloud data of the target object robustly.
VL  - 10
IS  - 1
ER  -

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Author Information

Yang Zhongfan

School of Automation & Information Engineering, Sichuan University of Science & Engineering, Yibin, China
Wang Xiaogang

School of Automation & Information Engineering, Sichuan University of Science & Engineering, Yibin, China
Hou Jing

School of Automation & Information Engineering, Sichuan University of Science & Engineering, Yibin, China

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Plain Text BibTeX RIS

APA Style

Yang Zhongfan, Wang Xiaogang, Hou Jing. (2022). Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors. International Journal of Sensors and Sensor Networks, 10(1), 1-6. https://doi.org/10.11648/j.ijssn.20221001.11

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ACS Style

Yang Zhongfan; Wang Xiaogang; Hou Jing. Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors. Int. J. Sens. Sens. Netw. 2022, 10(1), 1-6. doi: 10.11648/j.ijssn.20221001.11

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AMA Style

Yang Zhongfan, Wang Xiaogang, Hou Jing. Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors. Int J Sens Sens Netw. 2022;10(1):1-6. doi: 10.11648/j.ijssn.20221001.11

Copy | Download

@article{10.11648/j.ijssn.20221001.11,
  author = {Yang Zhongfan and Wang Xiaogang and Hou Jing},
  title = {Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {10},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijssn.20221001.11},
  url = {https://doi.org/10.11648/j.ijssn.20221001.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20221001.11},
  abstract = {As a consumer-grade portable depth image data acquisition device, the depth camera is widely used in the field of computer vision, such as slam, autonomous driving, environment perception, etc. However, due to the limitation of the device angle, the complete 3D point cloud of the target cannot be obtained at one time. Point cloud registration can complete the overlap of two frames of point clouds. Therefore, a multi-frame point cloud fusion method based on key points and registration is proposed. First, the point cloud is calculated on the depth map obtained by the depth camera, and then an improved point cloud filtering algorithm based on the normal vector inner cumulus is used to remove the background and noise points. Secondly, four key point detection algorithms and three registration algorithms with different principles are applied to the point cloud data obtained by the depth camera, and the applicable scenarios and limitations of each algorithm are analyzed. Finally, a multi-frame point cloud fusion algorithm is used to splice the point clouds, and the redundant points after splicing are filtered out to obtain a complete point cloud of the object. The experimental verification of the target object using the depth camera shows that the proposed method can obtain the complete point cloud data of the target object robustly.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Multi-frame Point Cloud Fusion Method Based on Depth Camera Sensors
AU  - Yang Zhongfan
AU  - Wang Xiaogang
AU  - Hou Jing
Y1  - 2022/01/15
PY  - 2022
N1  - https://doi.org/10.11648/j.ijssn.20221001.11
DO  - 10.11648/j.ijssn.20221001.11
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2329-1788
UR  - https://doi.org/10.11648/j.ijssn.20221001.11
AB  - As a consumer-grade portable depth image data acquisition device, the depth camera is widely used in the field of computer vision, such as slam, autonomous driving, environment perception, etc. However, due to the limitation of the device angle, the complete 3D point cloud of the target cannot be obtained at one time. Point cloud registration can complete the overlap of two frames of point clouds. Therefore, a multi-frame point cloud fusion method based on key points and registration is proposed. First, the point cloud is calculated on the depth map obtained by the depth camera, and then an improved point cloud filtering algorithm based on the normal vector inner cumulus is used to remove the background and noise points. Secondly, four key point detection algorithms and three registration algorithms with different principles are applied to the point cloud data obtained by the depth camera, and the applicable scenarios and limitations of each algorithm are analyzed. Finally, a multi-frame point cloud fusion algorithm is used to splice the point clouds, and the redundant points after splicing are filtered out to obtain a complete point cloud of the object. The experimental verification of the target object using the depth camera shows that the proposed method can obtain the complete point cloud data of the target object robustly.
VL  - 10
IS  - 1
ER  -

Copy | Download