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@INPROCEEDINGS{moreno2013erode,
     author = {Moreno, Francisco-Angel and Blanco, José-Luis and Gonzalez-Jimenez, Javier},
      month = {{{may}}},
      title = {ERODE: An Efficient and Robust Outlier Detector and its Application to Stereovisual Odometry},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
       year = {2013},
  publisher = {IEEE},
   location = {Karlsruhe},
       issn = {1050-4729},
       isbn = {978-1-4673-5641-1},
        url = {http://mapir.isa.uma.es/mapirwebsite/index.php/mapir-downloads/papers/211},
        doi = {10.1109/ICRA.2013.6631245},
   abstract = {This paper presents ERODE, an efficient outlier detector with a quality similar to that of standard RANSAC but at a fraction of its computational cost. In contrast to RANSAC-based methods which follow a hypothesis-and-verify approach, ERODE employs instead the whole set of observations together with a robust kernel to perform robustified least-squares minimization. Our proposal has important practical applications among computer vision problems, which we demonstrate with stereovisual odometry experiments with both simulated and real data.},
      pages = {4676--4682}
}