RANSAC has also been tailored for recursive state estimation applications, where the input measurements are corrupted by outliers and Kalman filter approaches, which rely on a Gaussian distribution of the measurement error, are doomed to fail. Such an approach is dubbed KALMANSAC.A. Vedaldi, H. Jin, P. Favaro, and S. Soatto, KALMANSAC: Robust filtering by consensus, Proceedings of the International Conference on Computer Vision (ICCV), vol. 1, 2005, pp. 633–640
