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Performance Analysis of Optimization Method and Filtering Method for Feature-based Monocular Visual SLAM특징점 기반 단안 영상 SLAM의 최적화 기법 및 필터링 기법 성능 분석
- Authors
- Jeon, Jin-Seok; Kim, Hyo-Joong; Shim, Duk-Sun
- Issue Date
- Jan-2019
- Publisher
- Korean Institute of Electrical Engineers
- Keywords
- Camera image; Feature point; Kalman filter; Monocular SLAM; Optimization; RANSAC
- Citation
- Transactions of the Korean Institute of Electrical Engineers, v.68, no.1, pp 182 - 188
- Pages
- 7
- Journal Title
- Transactions of the Korean Institute of Electrical Engineers
- Volume
- 68
- Number
- 1
- Start Page
- 182
- End Page
- 188
- ISSN
- 1975-8359
2287-4364
- Abstract
- Autonomous mobile robots need SLAM (simultaneous localization and mapping) to look for the location and simultaneously to make the map around the location. In order to achieve visual SLAM, it is necessary to form an algorithm that detects and extracts feature points from camera images, and gets the camera pose and 3D points of the features. In this paper, we propose MPROSAC algorithm which combines MSAC and PROSAC, and compare the performance of optimization method and the filtering method for feature-based monocular visual SLAM. Sparse Bundle Adjustment (SBA) is used for the optimization method and the extended Kalman filter is used for the filtering method. Copyright © The Korean Institute of Electrical Engineers.
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