Data Association in Robot Simultaneous Localization and Mapping with Monocular Vision
| Autor: | Sheng-Hsien Cheng, 鄭聖賢 |
|---|---|
| Rok vydání: | 2010 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 98 In this thesis, the algorithms of map building are developed for a simultaneous localization and mapping (SLAM) system with monocular vision. The algorithms include the method of feature detection and data association in between the measured features and features in the map. For the detection of image features, the speeded-up robust features (SURF) with high-dimensional description vectors are utilized to describe the map features, and build the feature-based map. In data association, a tracking window is planned based on the prediction of map features in spatial location, and then the nearest neighbor method is employed to match the high-dimensional descriptor vector of the measured features with that of the features in the map. The Lucas-Kanade algorithm is also utilized to improve the performance of feature tracking in the image plane. Finally, the developed SLAM system with monocular vision is integrated with the function module of detection and tracking of moving objects (DATMO), in order to perform the tasks of simultaneous localization mapping, and moving object tracking (SLAMMOT). |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|