A Multiple View 3D Registration Algorithm with Statistical Error Modeling

概要

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The contribution of the paper is two-fold:Firstly, a review of the point set registration literature is given, and secondly, a novel covariance weighted least squares formulation of the multiple vew point set registration problem is presented. Point data for surface registration is commonly obtained by non-contact, 3D surface sensors such as scanning laser range finders or structured light systems. Our formulation allows tha specification of anisotropic and heteroscedastic(point dependent)3D noise distributions for each measured point. In contrast, previous algorithms have generally assumed an isotropic sensor noise model, which cannot accurately describe the sensor noise characteristics. For cases where the point measurements are heteroscedastically and anisotropically distributed, registration results obtained with the proposed method show improved accuracy over those produced by an unweighted least squares formulation. Results are presented for both synthetic and real data sets to demonstrate the accuracy and effectiveness of the proposed technique.
社団法人電子情報通信学会の論文
2000-08-25