| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 95 === In the application of root Simultaneous Localization and Mapping (SLAM) in a large scale environment, it remains a challenge to resolve the obstacle of the inevitable computational burden on the filtering scheme imposed by the excessive number of landmarks.
This obstacle maily attributes to two facts: one is that the selection scheme is not sufficiently stringent, thus resulting in the inclusion of valueless localization landmarks during the environment observation process; the other is the mathematical characteristic of the filter, i.e. the computational complexity is proportional to the number of landmarks. In this thesis, we propose a visual front-end system integrating the speed-up robust feature extraction (SURF Extraction) and Inverse Depth Initialization to efficiently and effectively select robust static landmark for the information of localization and mapping and significantly reduce the uncertainty of the large exploration environment under the presumption of re-observation of the map. Furthermore, we extend the sparse linearization information filtering algorithm to the application of visual sensor. In the SLAM of laser, it has been proved the adoption of sparse linearization information filter effectively improve the computational efficiency. The performance and reliability is validated by the simulation and experiments.
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