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Size character optimization for measurement system with binocular vision and optical elements based on local particle swarm method

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Abstract

The size character, which represents the relationship of the size variables of a binocular vision model, is studied to determine the optimal structure of the measurement system. An optimal objective function is constructed to minimize the system area. The optimal solution with the constraint of the virtual baseline distance is obtained from the particle swarm optimization (PSO) algorithm. A case study shows that when the virtual baseline is 1300 mm, the optimal parameters are: the real baseline distance is 600 mm, the bottom distance between the two smaller mirrors is 120 mm, the distance from a smaller mirror to the camera is 600 mm, the distance from a larger mirror to the camera is 700 mm, the angle between the smaller mirror and baseline is 15°, the angle between the large mirror and baseline is 30°, larger mirror is 500 mm long, then the optimal system area is 0.838 m2.

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Acknowledgments

The authors express their gratitude to the supports of National Natural Science Foundation of China under Grant No. 51205164, No. 51478204 and No. 51405075, and Jilin Province Science Foundation for Youths, under Grant No. 20130522154JH.

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Authors and Affiliations

  1. Department of Vehicle Application Engineering, Traffic and Transportation College, Nanling Campus, Jilin University, Renmin Street 5988#, Changchun, 130025, Jilin, People’s Republic of China

    Guan Xu, Xue Lu, Jian Su & Lina Sun

  2. Mechanical Science and Engineering College, Nanling Campus, Jilin University, Renmin Street 5988#, Changchun, 130025, Jilin, People’s Republic of China

    Xiaotao Li

  3. Transportation College, Northeast Forestry University, Harbin, 150040, Heilongjiang, People’s Republic of China

    Guangdong Tian

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  1. Guan Xu
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  2. Xue Lu
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  3. Xiaotao Li
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  4. Jian Su
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  5. Guangdong Tian
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Correspondence to Xiaotao Li.

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Xu, G., Lu, X., Li, X. et al. Size character optimization for measurement system with binocular vision and optical elements based on local particle swarm method. Opt Rev 22, 58–64 (2015). https://doi.org/10.1007/s10043-015-0057-x

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  • DOI: https://doi.org/10.1007/s10043-015-0057-x

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