Underwater Camera Calibration Method Based on Improved Slime Mold Algorithm

Shuai Du, Yun Zhu, Jianyu Wang, Jieping Yu and Jia Guo
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Shuai Du: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Yun Zhu: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Jianyu Wang: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Jieping Yu: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Jia Guo: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Sustainability, 2022, vol. 14, issue 10, 1-14

Abstract: The maritime transportation line is the lifeblood of the national economy. Transportation facilities and their construction equipment need to obtain the environmental parameters of relevant sea areas, and underwater robots are the first type of auxiliary equipment for underwater road construction. Considering that the construction of underwater transportation facilities puts forward higher requirements for the observation accuracy of underwater robots, the reliability of internal and external parameter calibration of underwater cameras directly affects the accuracy of underwater positioning and measurement. In order to improve the calibration accuracy of underwater cameras, this paper establishes a real underwater camera calibration image data set, integrates the optimal neighborhood disturbance and reverse learning strategy on the basis of the slime mold optimization algorithm, optimizes the calibration results of Zhang’s traditional calibration method, and compares the optimization results and reprojection error of the ORSMA algorithm with Zhang’s calibration method the SMA algorithm, SOA algorithm and PSO algorithm to verify the accuracy and effectiveness of the proposed algorithm.

Keywords: camera calibration; slime mold optimization algorithm (SMA); optimal neighborhood perturbation (ONP); reverse learning strategy (OBL) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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