碩士 / 國立臺灣大學 / 生物產業機電工程學研究所 / 103 / With the increasing popularity of 3D printing and rapid development of virtual reality, there is a large demand for good 3D model reconstruction mothed. This research aims to develop a simultaneous localization and mapping(SLAM)-based autonomous navigation scene reconstruction robot system. The system consists of a robot moving platform and a spatial information collector. The robot moving platform performs the SLAM-based autonomous navigation. By inputing the location information and map from SLAM, the A* path planning algorithm can achieve autonomous navigation. Spatial information collector collects the 3D spatial information and the color information simultaneously. This research adopts the color supported generalized iterative closest point (color supported GICP) method as the scene registration algorithm for 3D model reconstruction. Compared to classic point-to-point frame iteractive closest point (ICP), the color supported GICP is plane-to-plane frame approach which is designed to solve the sampling point cloud mismatching problem and the violation of fully overlapped region hypothesis. With extra color information, the color supported GICP can converge faster than ones that do not integrate color information. The experimental results show that GICP are capable of converging to nearly the same error that ICP does with fewer corresponding points. The color supported GICP reaches the highest converge speed when the weight of color information is 0.2. The extra color information can help the color supported GICP converging faster than the GICP with 14 less iterations or 60.9% faster time. There are 2 indoor scenes and 4 outdoor scenes tested in this research. As indoor and outdoor scenes have different scale, the best minimum search radius of outdoor case is 0.05 m, and the best minimum search radius of indoor case is 0.001 m. This research tests the performance of the 4 algorthms (ICP, color supported ICP, GICP, and color supported GICP) in these scenes. The result shows that bushes and fractured point clouds will slow down the converge speed of GICP. But in indoor cases, GICP converges faster than ICP. All the cases show that color supported GICP converges fastest among 4 algorithms. Although ICP and color supported ICP can converge to lower error than GICP and color supported GICP, GICP and color supported GICP converge faster than ICP and color supported ICP in indoor cases.
| Identifer | oai:union.ndltd.org:TW/103NTU05415009 |
| Date | January 2015 |
| Creators | Rong-Siou Lee, 李榕修 |
| Contributors | Ta-Te Lin, 林達德 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 89 |
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