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On Minimizing the Probability of Large Errors in Robust Point Cloud Registration

In solving a model fitting problem, the existence of outliers in the set of measurements can have a devastating effect on the solution accuracy. Traditionally, in order to overcome this problem, robust point cloud registration algorithms are composed of transformation hypothesis generation, followed...

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書誌詳細
主要な著者: AMIT EFRAIM, Joseph M. Francos
フォーマット: Artigo
言語:Inglês
出版事項: IEEE 2024-01-01
シリーズ:IEEE Open Journal of Signal Processing
主題:
オンライン・アクセス:https://ieeexplore.ieee.org/document/10345750/
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