  We review the most recent RANSAC-like hypothesize-and-verify robust
estimators. The best performing ones are combined to create a state-of-the-art
version of the Universal Sample Consensus (USAC) algorithm. A recent objective
is to implement a modular and optimized framework, making future RANSAC modules
easy to be included. The proposed method, USACv20, is tested on eight publicly
available real-world datasets, estimating homographies, fundamental and
essential matrices. On average, USACv20 leads to the most geometrically
accurate models and it is the fastest in comparison to the state-of-the-art
robust estimators. All reported properties improved performance of original
USAC algorithm significantly. The pipeline will be made available after
publication.
