Abstract
The local invariant features SURF (Speeded Up Robust Features) is introduced into the robot visual recognition field to solve scale changes, rotation, perspective changes, changes in illumination and other problems. A Speeded up SURF (SSURF) algorithm is proposed to meet the needs of robot visual identification. In SSURF algorithms, the main direction determination step of SURF algorithm is modified which make the search scope of the main direction becomes {-α, +α} (0 ≤ a ≤ 30̊) from the original scope 360̊ According to compressed sensing ideas and interest points distribution histogram, the main scale search space is selected to improve the interest points searching step of SURF algorithm, so the interest points searching time-consuming is reduced. Matching the sample object and the scene using SSURF descriptor, and positioning the target position in the scene and giving ROI(region of interest). Experimental results in the autonomous mobile robot platform show that the proposed method significantly improves the speed of the robot to identify the target object, and proved robust to the scale changes, rotation, perspective changes, changes in illumination.
Original language | English |
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Pages | 581-586 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2013 |
Event | 2013 Chinese Automation Congress, CAC 2013 - Changsha, China Duration: 7 Nov 2013 → 8 Nov 2013 |
Conference
Conference | 2013 Chinese Automation Congress, CAC 2013 |
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Country/Territory | China |
City | Changsha |
Period | 7/11/13 → 8/11/13 |
Keywords
- Feature matching
- Local invariant features
- Object recognition
- SURF