Cost-Effective Unbiased Straight-Line Fitting to Multi-Viewpoint Range Data (Special Section of Selected Papers from the 9th Karuizawa Workshop on Circuits and Systems)

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概要

• 論文の詳細を見る

• The present paper clarifies that the variance of the maximum likelihood estimator (MLE) of a parameter does not reach the Cramer-Rao lower bound (CRLB) when fitting a straight-line to observed two-dimensional data. In addition, the variance of the MLE can be shown to be equal to the CRLB only if observed noise reduces to a one-dimensional Gaussian variable. For most practical applications, it can be assumed that noise is added only to the range direction. In this case, the MLE is clearly an asymptotically effective estimator. However, even if we assume such a noise model, ML line-fitting to the data from many points of view has a high computational cost. The present paper proposes an alternative fitting method in order to provide a cost-effective unbiased estimator. The reliability of this new method is analyzed statistically and by computer simulation.

• 社団法人電子情報通信学会の論文
• 1997-03-25

著者

• SUZUKI Toshio

  Department of Animal Science, Faculty of Agriculture, Utsunomiya University

• Tagawa Norio

  Department Of Electrical Engineering Tokyo Metropolitan University

• SUZUKI TATSUYA

  Graduate School of Engineering, Nagoya University

• MORIYA Tadashi

  Department of Electrical Engineering, Tokyo Metropolitan University

• Moriya Tadashi

  Department Of Electrical Engineering Tokyo Metropolitan University

• Suzuki Tatsuya

  Department Of Electronic-mechanical Engineering Graduate School Of Engineering Nagoya University

• Suzuki Tatsuya

  Department Of Mechanical Science And Engineering Graduate School Of Engineering Nagoya University

• Tagawa Norio

  Depart. Of Mechanical Engineering High Technology Research Center Kansai University

• Suzuki T

  Shizuoka Univ. Hamamatsu‐shi Jpn

• Suzuki Toshio

  Department Of Agronomy Faculty Of Agriculture Tohoku University

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