RANSAC Algorithm and Its Various Improved Versions

This article presents the GML_RANSAC_Matlab_Toolbox_v0.2 implementation of RANSAC algorithm and its various improved versions, along with detailed analysis of simulation results. RANSAC (Random Sample Consensus) is an estimation algorithm based on random sampling and consensus validation, primarily implemented through iterative random sampling of minimal data points to generate candidate models. Its key advantage lies in robust parameter estimation for data models even in the presence of significant noise and outliers. The implementation typically involves core functions for random subset selection, model fitting, and inlier counting using distance thresholds. We provide comprehensive explanations of RANSAC's fundamental principles and workflow, including the algorithmic steps of hypothesis generation, model validation, and consensus set maximization. The analysis covers both strengths and limitations of the standard RANSAC approach. Furthermore, we introduce several enhanced variants including: - PROSAC (PROgressive SAMPLE Consensus): Utilizes quality-based sampling strategy instead of pure random selection - MLESAC (Maximum Likelihood Estimation SAMPLE Consensus): Incorporates probability-based weighting using maximum likelihood estimation - LMedS (Least Median of Squares): Employs median-based robust estimation rather than consensus counting Each improved algorithm's implementation specifics are discussed, focusing on their modified sampling strategies, scoring mechanisms, and convergence properties. Performance comparisons are conducted through systematic experiments evaluating computational efficiency, accuracy metrics, and outlier rejection capabilities. Finally, simulation experiments validate algorithm effectiveness using synthetic and real-world datasets, with detailed result analysis covering success rates, parameter estimation errors, and computational complexity to facilitate better understanding and practical application of these robust estimation techniques.