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SIFT and SURF Performance Evaluation for Mobile Robot-Monocular Visual Odometry
| Content Provider | Semantic Scholar |
|---|---|
| Author | Benseddik, Houssem Eddine Djekoune, Oualid Belhocine, Mahmoud |
| Copyright Year | 2014 |
| Abstract | Visual odometry is the process of estimating the motion of mobile through the camera attached to it, by matching point features between pairs of consecutive image frames. For mobile robots, a reliable method for comparing images can constitute a key component for localization and motion estimation tasks. In this paper, we study and compare the SIFT and SURF detector/ descriptor in terms of accurate motion determination and runtime efficiency in context the mobile robot-monocular visual odometry. We evaluate the performance of these detectors/ descriptors from the repeatability, recall, precision and cost of computation. To estimate the relative pose of camera from outlier-contaminated feature correspondences, the essential matrix and inlier set is estimated using RANSAC. Experimental results demonstrate that SURF, outperform the SIFT, in both accuracy and speed. |
| Starting Page | 70 |
| Ending Page | 76 |
| Page Count | 7 |
| File Format | PDF HTM / HTML |
| DOI | 10.12720/joig.2.1.70-76 |
| Alternate Webpage(s) | http://www.joig.org/uploadfile/2014/0516/20140516034436840.pdf |
| Alternate Webpage(s) | https://doi.org/10.12720/joig.2.1.70-76 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
