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基于M-估計的線性化穩(wěn)健配準算法研究
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航空科學基金資助項目(20131625)、江蘇省研究生培養(yǎng)創(chuàng)新工程資助項目(KYLX—0309)、民機專項科研資助項目(MJ—G—2011—24)和國家自然科學基金資助項目(11326088)


Linearized Robust Registration Algorithm Based on M-estimation
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    摘要:

    復雜曲面在制造中的廣泛應用對曲面配準技術提出了新的要求,特別是不同區(qū)域精度存在差異的復雜曲面配準問題日益突出。為了穩(wěn)健估計思想推廣到不同區(qū)域精度存在差異的復雜曲面配準,給出了基于M-估計的一種穩(wěn)健配準算法。該算法利用M估計子削弱復雜曲面低精度數(shù)據(jù)對配準結果的影響,但是這一模型目標函數(shù)是高度非線性的分段函數(shù),求解效率不高?,F(xiàn)有配準方法已能夠迅速獲得較好初始位置,因此利用Taylor展式線性逼近偏差函數(shù),得到配準問題M-估計的線性化模型,提高了配準模型估計效率。每步迭代利用F-范數(shù)最小逼近旋轉矩陣。對仿真數(shù)據(jù)和實測葉片數(shù)據(jù)進行試驗,結果證明,對精度存在差異的復雜曲面所提算法比最近點迭代算法更加合理。

    Abstract:

    Rapid and wide application of the complex surface in modern manufacturing makes new demands of the registration techniques on complex surface. Although significant progress has been made in complex surface registration, it remains a difficult problem in some situation. For a complex part with multiple freeform surfaces, the precision of measurement points often exists different in different surface regions due to a variety of measurement methods. Meanwhile, the manufacture precision in different regions is also not the same in complex manufacture process. Problems of registration on complex surface, have become increasingly prominent and new methods are bound to be found. Robust principle was generalized to the complex surface registration in which the precision difference existed in different surface regions. A robust registration was presented based on M-estimation. The effect of low precision measured data was weakened for the registration result by M-estimation functions. But the solving efficiency of the model was low due to the highly nonlinear and piecewise of the objective function. A good initial position was easily available with current registration method, and the error functions were linearly approximated by Taylor expansion when the rotation transform was slight. A linear registration model was found and the efficiency was improved. An approximation of the rotation matrix based on the minimization of Fibonacci norm was adopted in each iteration. Both theoretical and experimental results confirmed the stabilization and efficiency.

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譚高山,張麗艷,劉勝蘭,張濤.基于M-估計的線性化穩(wěn)健配準算法研究[J].農(nóng)業(yè)機械學報,2015,46(4):360-364,343. Tan Gaoshan, Zhang Liyan, Liu Shenglan, Zhang Tao. Linearized Robust Registration Algorithm Based on M-estimation[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(4):360-364,343.

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  • 收稿日期:2014-06-17
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  • 在線發(fā)布日期: 2015-04-10
  • 出版日期: 2015-04-10
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