碩士 / 國立清華大學 / 電機工程學系 / 101 / Simultaneous localization and mapping (SLAM) has long been a major topic of research in mobile robot navigation. In an unknown environment, the mobile robot can localize itself and build a map at the same time while navigating. The mobile robot estimates its location through the sensors it equips with. And the sensors for SLAM get more and more accurate from odometry to camera and laser range finder. Besides, there are a variety of methods developed to make SLAM more accurate and more efficient, such as Extended Kalman Filter (EKF), Particle Filter, and scan matching.
In this thesis, the SICK laser range finder gets the raw scan data as a set of 2D points and the EKF is used to estimate the position of the robot in the SLAM process. We incorporate raw scan data and scan matching algorithms into the EKF framework. That is, the landmarks (features), like point or line, in the original feature based EKF are replaced by the raw scan data. Then, the result of the scan matching algorithm is used as a measurement to replace the data association step in the original EKF framework. Scan matching uses Iterative Closest Point (ICP) algorithm to align the corresponding points between two scan data by rotation and translation iteratively until the two data sets are matched, that is, they partially represent a common shape.
In the thesis, the combination of EKF and scan matching makes the strengths of each offset the weaknesses of the other. Using the raw scan data as landmarks makes EKF do not need to rely on the geometric models and keeps more information for map building. The data association step of EKF can be replaced by using ICP algorithm to match between two scans without the need to store the scan history.
| Identifer | oai:union.ndltd.org:TW/101NTHU5442039 |
| Date | January 2013 |
| Creators | Wang, Mei-Yi, 王美懿 |
| Contributors | Chen, Yung-Chang, 陳永昌 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 49 |
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