Monocular Vision SLAM Based on Key Feature Points Selection

摘要：

Simultaneous localization and mapping (SLAM) is an key research content of robot autonomous navigation, the visual monocular SLAM based on Extend Kalman Filter(EKF) is one important method to handle this problem. But due to high computational complexity, it has strict limits on the number and stability of the feature points, traditional method selects few corners like or straight lines as feature points, and these methods limit the application scope of EKF-SLAM. This paper proposes a key points selection method based on SIFT(Scaleinvariant feature transform) feature point, on the assumption of relative uniform of the feature points’ distribution, through controlling the total number of feature points effectively, the applied restriction of the visual monocular EKF-SLAM is reduced. Experiments show that this feature point selection method has a high stability for different scenes, and improves the convergence velocity.

关键词： Robot EKF-SLAM Key point selection Monocular vision SIFT

作者: Eryong Wu Likun Zhao Yiping Guo Wenhui Zhou Qicong Wang

作者单位: Department of Computer Science & Technology Hangzhou Dianzi University,Hangzhou 310018,China Department of Computer Science Xiamen University Xiamen,361005,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-5

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）