Geometric Inlier Selection for Robust Rigid Registration With Application to Blade Surfaces

Abstract

Robotic 3D measurement for evaluating the final shape of blades can improve product quality and reduce fabrication costs. However, large or complex objects require multiple scans from different viewpoints to capture the entire object. Errors in registration artificially introduce defects and surface discontinuties in the reconstructed object model. To address the sources of inaccurate registration for methods relying on pairwise associated points, this manuscript describes an inlier selection approach based on the geometric properties of rigid registration, called RiGID. It readily applies to registration strategies that use 3D interest points and descriptors to associate data across overlapping point clouds. RiGID converts geometric constraint violations into distributions over geometric error values. Analysis of the distributions provides a deterministic and data adaptive way to identify inlier sets. RiGID is an efficient preprocessor to rigid registration. It reduces the amount of input data and improves the inlier ratio, which translates to more efficient and accurate registration. Evaluation of the method shows a higher inlier rate and lower registration error compared to baseline outlier rejection methods. The approach is well suited to surfaces with smooth regions and a high percentage of outliers (up to 90\%), which engine and turbine blades exhibit.