Fast visual SLAM method based on point and line features

被引：0
|
作者：
Ma X. [1 ]
Liang X.-W. [1 ]
Cai J.-Y. [1 ]
机构：
[1] School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2021年 / 55卷 / 02期
关键词：
Geometric constraints; Point and line features; RGB-D; Time efficiency; Visual simultaneous localization and mapping (SLAM);
D O I：
10.3785/j.issn.1008-973X.2021.02.021
中图分类号：
学科分类号：
摘要：
A fast simultaneous localization and mapping (SLAM) algorithm based on point and line features was proposed in order to improve the localization accuracy and the robustness of SLAM system under RGB-D cameras in low-textured scenes. During the tracking of non-keyframes, point feature matching was performed based on descriptors, and line feature matching was performed based on geometric constraints. When a new keyframe was inserted, the descriptors of the line features were calculated to complete the line feature matching between the keyframes, and the line feature triangulation algorithm was used to generate map lines. The real-time performance of the SLAM system was improved by reducing the amount of calculation in the line feature matching process. In addition, virtual right-eye lines were constructed using the depth measurement information of line features, and a new method for calculating reprojection errors of line features was proposed. Experimental results on public datasets showed that compared with mainstream methods such as ORB-SLAM2, the proposed algorithm improved the localization accuracy of the RGB-D SLAM system in low-textured scenes. The time efficiency of the proposed algorithm was improved by about 20% compared with traditional SLAM method combining point and line features. Copyright ©2021 Journal of Zhejiang University (Engineering Science). All rights reserved.
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页码：402 / 409
页数：7
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