RANSAC Algorithm Implementation

This code demonstrates the detailed implementation steps of the RANSAC (Random Sample Consensus) algorithm. Understanding these steps is highly beneficial for learning RANSAC. The implementation begins with data preprocessing, which is crucial for RANSAC's reliability - typically involving data normalization and outlier detection. Next, the code shows the random sample selection process, a core component of RANSAC where subsets are randomly chosen from the dataset using functions like randomSampling(). The model fitting process follows, where algorithms such as linearRegression() or modelEstimation() transform the random samples into mathematical models. Finally, the model validation process evaluates whether the generated model meets requirements through methods like errorCalculation() and consensusSet identification. These step-by-step explanations with corresponding code functions help deepen understanding of RANSAC's implementation process, enabling better application to solve real-world problems involving robust parameter estimation and outlier rejection.