Abstract: Robust estimation is a basic technology in geometric processing and survey adjustment. Traditional iteratively reweighted least squares (IRLS) cannot handle problems with high outlier rates (≥50%); Random sampling consensus (RANSAC) type algorithms can only obtain approximate solutions and are time consuming. This paper proposes a progressively optimized scale-adaptive Cauchy robust estimation model. First, a scale parameter is introduced into the typical Cauchy kernel function to control its robustness. Second, the proposed method uses the control parameter to filter out some observations with the large residuals in each iteration and reduce the true outlier rate. Then, a “coarse to fine” IRLS method is used for optimization in a progressive manner. In the iterative process, the control parameter is continuously reduced to improve the robustness. This paper also applies the proposed model in several important tasks of photogrammetry, including mismatch removal, image orientation, and point cloud registration. Extensive experiments show that the proposed model is robust to more than 80% outliers when the gross errors conform to an approximately uniform or random distribution, and is 2~3 orders of magnitude faster than RANSAC.

Key words: robust estimation, gross errors, image matching, space resection, point cloud registration