Collaborative Object Detection and Distributed Fusion Based on Dual Camera

Shunli Gao; Huiping Li

doi:10.1109/ONCON60463.2023.10431221

Collaborative Object Detection and Distributed Fusion Based on Dual Camera

Shunli Gao
, Huiping Li

School of Marine Science and Technology

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This article investigates the collaborative localization and distributed fusion of multiple unmanned aerial vehicles based on cameras, including object detection algorithms, state estimation based on unscented Kalman filters(UKF), and data fusion. Starting from introducing the model we built in Gazebo and the 3D target localization implemented in real-time operating system(ROS), we analyzed the characteristics of the target detection results and the necessity for improvement. In order to obtain more accurate target positions, we proposed a collaborative localization algorithm based on post-fusion, which was processed and validated in MATLAB. Finally, the effectiveness of our algorithm was demonstrated through comparison.

Original language	English
Title of host publication	2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350357974
DOIs	https://doi.org/10.1109/ONCON60463.2023.10431221
State	Published - 2023
Event	2nd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2023 - Virtual, Online, United States Duration: 8 Dec 2023 → 10 Dec 2023

Publication series

Name	2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023

Conference

Conference	2nd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2023
Country/Territory	United States
City	Virtual, Online
Period	8/12/23 → 10/12/23

Keywords

camera perception
distributed fusion
object detection
unscented Kalman filter

Access to Document

10.1109/ONCON60463.2023.10431221

Cite this

Gao, S., & Li, H. (2023). Collaborative Object Detection and Distributed Fusion Based on Dual Camera. In 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023 (2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ONCON60463.2023.10431221

@inproceedings{7347a98e4a9f4609abc7f72274406860,

title = "Collaborative Object Detection and Distributed Fusion Based on Dual Camera",

abstract = "This article investigates the collaborative localization and distributed fusion of multiple unmanned aerial vehicles based on cameras, including object detection algorithms, state estimation based on unscented Kalman filters(UKF), and data fusion. Starting from introducing the model we built in Gazebo and the 3D target localization implemented in real-time operating system(ROS), we analyzed the characteristics of the target detection results and the necessity for improvement. In order to obtain more accurate target positions, we proposed a collaborative localization algorithm based on post-fusion, which was processed and validated in MATLAB. Finally, the effectiveness of our algorithm was demonstrated through comparison.",

keywords = "camera perception, distributed fusion, object detection, unscented Kalman filter",

author = "Shunli Gao and Huiping Li",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2023 ; Conference date: 08-12-2023 Through 10-12-2023",

year = "2023",

doi = "10.1109/ONCON60463.2023.10431221",

language = "英语",

series = "2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023",

}

Gao, S & Li, H 2023, Collaborative Object Detection and Distributed Fusion Based on Dual Camera. in 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023. 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2023, Virtual, Online, United States, 8/12/23. https://doi.org/10.1109/ONCON60463.2023.10431221

Collaborative Object Detection and Distributed Fusion Based on Dual Camera. / Gao, Shunli; Li, Huiping.
2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Collaborative Object Detection and Distributed Fusion Based on Dual Camera

AU - Gao, Shunli

AU - Li, Huiping

PY - 2023

Y1 - 2023

N2 - This article investigates the collaborative localization and distributed fusion of multiple unmanned aerial vehicles based on cameras, including object detection algorithms, state estimation based on unscented Kalman filters(UKF), and data fusion. Starting from introducing the model we built in Gazebo and the 3D target localization implemented in real-time operating system(ROS), we analyzed the characteristics of the target detection results and the necessity for improvement. In order to obtain more accurate target positions, we proposed a collaborative localization algorithm based on post-fusion, which was processed and validated in MATLAB. Finally, the effectiveness of our algorithm was demonstrated through comparison.

AB - This article investigates the collaborative localization and distributed fusion of multiple unmanned aerial vehicles based on cameras, including object detection algorithms, state estimation based on unscented Kalman filters(UKF), and data fusion. Starting from introducing the model we built in Gazebo and the 3D target localization implemented in real-time operating system(ROS), we analyzed the characteristics of the target detection results and the necessity for improvement. In order to obtain more accurate target positions, we proposed a collaborative localization algorithm based on post-fusion, which was processed and validated in MATLAB. Finally, the effectiveness of our algorithm was demonstrated through comparison.

KW - camera perception

KW - distributed fusion

KW - object detection

KW - unscented Kalman filter

UR - https://www.scopus.com/pages/publications/85186747792

U2 - 10.1109/ONCON60463.2023.10431221

DO - 10.1109/ONCON60463.2023.10431221

M3 - 会议稿件

AN - SCOPUS:85186747792

T3 - 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023

BT - 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference, ONCON 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2023

Y2 - 8 December 2023 through 10 December 2023

ER -

Collaborative Object Detection and Distributed Fusion Based on Dual Camera

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