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Open AccessArticle
A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction
by
Hao Huang
Hao Huang
Hao Huang graduated from Shandong Agricultural University in 2017 and is currently studying at the a [...]
Hao Huang graduated from Shandong Agricultural University in 2017 and is currently studying at the School of Mechanical Engineering, Chang'an University, pursuing a doctoral degree. His research topics mainly include construction machinery, intelligence, unmanned technology, and high-precision positioning technology.
,
Yongbiao Hu
Yongbiao Hu and
Xuebin Wang
Xuebin Wang *
National Engineering Laboratory for Highway Maintenance Equipment, Chang’an University, Xi’an 710064, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(13), 4408; https://doi.org/10.3390/s24134408 (registering DOI)
Submission received: 27 May 2024
/
Revised: 3 July 2024
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Accepted: 5 July 2024
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Published: 7 July 2024
Abstract
As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels.
Share and Cite
MDPI and ACS Style
Huang, H.; Hu, Y.; Wang, X.
A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction. Sensors 2024, 24, 4408.
https://doi.org/10.3390/s24134408
AMA Style
Huang H, Hu Y, Wang X.
A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction. Sensors. 2024; 24(13):4408.
https://doi.org/10.3390/s24134408
Chicago/Turabian Style
Huang, Hao, Yongbiao Hu, and Xuebin Wang.
2024. "A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction" Sensors 24, no. 13: 4408.
https://doi.org/10.3390/s24134408
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