Robot robust object recognition based on fast SURF feature matching

Mingfang Du, Junzheng Wang, Jing Li, Haiqing Cao, Guangtao Cui, Jianjun Fang, Ji Lv, Xusheng Chen

Research output: Contribution to conferencePaperpeer-review

11 Citations (Scopus)

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 languageEnglish
Pages581-586
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 Chinese Automation Congress, CAC 2013 - Changsha, China
Duration: 7 Nov 20138 Nov 2013

Conference

Conference2013 Chinese Automation Congress, CAC 2013
Country/TerritoryChina
CityChangsha
Period7/11/138/11/13

Keywords

  • Feature matching
  • Local invariant features
  • Object recognition
  • SURF

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