In this thesis work, we evaluate an object recognition system for an autonomous wheel loader, to detect objects in its vicinity, in particular an articulated hauler truck, by using an interest point extraction method that explicitly considers object borders information, combined with a feature descriptor known as Normal Aligned Radial Features (NARF) in 3D point cloud data. The object recognition technique relies on extraction of NARF from range images (computed from point clouds) for both model(hauler) and the scene. The technique used is robust feature matching where the extracted model features are mapped on to the scene containing the model and then seeking for a best transformation that aligns the model with respect to the scene. In this context we conducted several experiments with many number of 3D scans obtained from the laser scanner mounted on the top of an autonomous wheel loader to analyze the accuracy of the object recognition system. Finally we demonstrated the results, as the system is able to recognize the hauler from any view point. vii
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:oru-26405 |
| Date | January 2011 |
| Creators | PRASANTH NANDANAVANAM, MANO |
| Publisher | Örebro universitet, Institutionen för naturvetenskap och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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