Highly Robust Estimator Using a Case-dependent Residual Distribution Model

Thanh Ngo Trung, Nagahara Hajime, Sagawa Ryusuke, Mukaigawa Yasuhiro, Yachida Masahiko, Yagi Yasushi

doi:10.11185/imt.5.77

The latest robust estimators usually take advantage of density estimation, such as kernel density estimation, to improve the robustness of inlier detection. However, the challenging problem for these systems is choosing the suitable smoothing parameter, which can result in the population of inliers being over- or under-estimated, and this, in turn, reduces the robustness of the estimation. To solve this problem, we propose a robust estimator that estimates an accurate inlier scale. The proposed method first carries out an analysis to figure out the residual distribution model using the obvious case-dependent constraint, the residual function. Then the proposed inlier scale estimator performs a global search for the scale producing the residual distribution that best fits the residual distribution model. Knowledge about the residual distribution model provides a major advantage that allows us to estimate the inlier scale correctly, thereby improving the estimation robustness. Experiments with various simulations and real data are carried out to validate our algorithm, which shows certain benefits compared with several of the latest robust estimators.

Highly Robust Estimator Using a Case-dependent Residual Distribution Model

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Highly Robust Estimator Using a Case-dependent Residual Distribution Model

説明

収録刊行物

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書き出し

問題の指摘

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