Maximum Consensus

Maximum consensus aims to identify the largest subset of consistent data points within a dataset, crucial for robust estimation in the presence of outliers. Current research focuses on developing efficient algorithms, such as those based on graph theory and optimization techniques like branch-and-bound and the alternating direction method of multipliers, to overcome the computational challenges associated with finding the global optimum, especially in high-dimensional problems. These advancements are significantly impacting fields like computer vision and robotics, enabling more reliable solutions for tasks such as point cloud registration, camera pose estimation, and autonomous vehicle localization. The development of more robust and scalable maximum consensus methods is driving progress in various applications requiring outlier rejection and accurate estimation from noisy data.