Consensus estimation is a research area that focuses on the development of algorithms and techniques for estimating values or parameters from a set of data points, where the data may be noisy, incomplete, or inconsistent. The goal of consensus estimation is to find a single estimate or value that best represents the underlying true value, even in the presence of uncertainty or disagreement among the data points. Researchers in this area may work on developing methods for combining multiple estimates from different sources, such as sensor data, opinions from experts, or measurements from different instruments. These methods often involve statistical techniques, optimization algorithms, or machine learning models to weigh the contributions of each data point and produce a more accurate and reliable estimate. Overall, consensus estimation is an important research area with applications in various fields such as computer vision, signal processing, social networks, and distributed systems. By improving the accuracy and reliability of estimates, researchers in this area aim to make better decisions, improve the quality of data analysis, and enhance the performance of systems that rely on estimates or parameter values.