ERODE: An Efficient and Robust Outlier Detector and its Application to Stereovisual Odometry
| Type of publication: | Inproceedings |
| Citation: | moreno2013erode |
| Booktitle: | IEEE International Conference on Robotics and Automation (ICRA) |
| Year: | 2013 |
| Month: | {{may}} |
| Pages: | 4676 -- 4682 |
| Publisher: | IEEE |
| Location: | Karlsruhe |
| ISSN: | 1050-4729 |
| ISBN: | 978-1-4673-5641-1 |
| URL: | http://mapir.isa.uma.es/mapirw... |
| 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. |
| Userfields: | http://mapir.isa.uma.es/famoreno/images/papers/erode_thumbnail.png |
| Keywords: | |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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