MATLAB Implementation of the RANSAC Algorithm

This program implements the RANSAC (Random Sample Consensus) algorithm, providing an optimal approach for learning and comprehending RANSAC through practical coding examples. The implementation follows the core RANSAC workflow: random sampling of minimal data points from the dataset, model fitting using the sampled subset, and evaluation of model consensus by counting inliers that fall within a specified error threshold. The algorithm iteratively selects the model with the highest number of inliers, effectively handling outliers in the dataset. Key implementation aspects include the random sampling mechanism, model parameter estimation functions, and inlier detection logic using distance thresholds. It's crucial to note that RANSAC is specifically designed for datasets containing outliers, and practical applications require careful adjustment of sampling parameters and iteration counts to achieve optimal results. Therefore, effective learning involves not only understanding the code structure and implementation details but also grasping the algorithm's mathematical principles, parameter tuning strategies, and appropriate application scenarios. The code demonstrates typical MATLAB programming patterns for handling numerical computations and iterative optimization processes.