ANTSAC: A Generic RANSAC Variant Using Principles of Ant Colony Algorithms

In this paper, we present a new variant of the well-known Random Sample Consensus (RANSAC) algorithm for robust estimation of model parameters. The idea of our method is based on a kind of volatile memory which is similar to the pheromone evaporation in the ant colony optimization algorithm. Therefore, we call our improved RANSAC like algorithm ANTSAC. We describe our new approach and the influence of its relevant parameters to the achieved performance in detail. ANTSAC is computationally efficient and convincingly easy to implement. It turns out that ANTSAC significantly outperforms RANSAC regarding the number of inliers after a given number of iterations. Further, we show that the advantage of ANTSAC increases with the complexity of the problem, i.e., with the number of model parameters, as well as with the relative number of outliers. ANTSAC is entirely generic, such that no further domain knowledge is required, as it is for many other RANSAC extensions. Nevertheless, we show that it is competitive to state-of-the-art methods even in domain specific scenarios.