Conventional cameras are usually small in their field of view (FOV) and make the observable region limited. Applications by such a vision system may also limit motion capabilities for robots when it comes to object tracking. Omnidirectional camera has a wide FOV which can obtain environmental data from all directions. In comparison with conventional cameras, the wide FOV of omnidirectional cameras reduces blind regions and improves tracking ability. In this thesis, we assume an omnidirectional camera is mounted on a moving platform, which travels with planar motion. By applying optical flow and CAMShift algorithm to track an object which is non-propelled and only subjected to gravity. Then, by parabolic fitting, least-square method and Levenberg-Marquardt method to predict the 3D coordinate of the object at the current instant and the next instant, we can finally predict the position of the drop point and drive the moving platform to meet the object at the drop point. The tracking operation and drop point prediction can be successfully achieved even if the camera is under planar motion and rotation.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0829111-231710 |
| Date | 29 August 2011 |
| Creators | Hsu, Chiang-Hao |
| Contributors | Cheng, Chi-Cheng, Perng, Jau-Woei, Her, Innchyn |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0829111-231710 |
| Rights | user_define, Copyright information available at source archive |
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