论文标题
强大的钥匙框架立体声视觉大满贯具有低阈值点和线路功能
Robust Key-Frame Stereo Visual SLAM with low-threshold Point and Line Features
论文作者
论文摘要
在本文中，我们开发了一个强大，有效的视觉大满贯系统，该系统利用了低阈值，基线线和闭环钥匙帧功能的空间抑制。使用ORB-SLAM2，我们的方法包括立体声匹配，框架跟踪，本地捆绑包调整以及线和点全局束调整。特别是，我们根据基线贡献了重新注射。融合系统中的线路会消耗巨大的时间，我们将分布点到利用特征点的空间抑制的时间减少。此外，低阈值要点在处理低纹理方面可能更有效。为了克服跟踪钥匙帧的冗余问题，提出了有效且稳健的闭环跟踪钥匙框架。所提出的SLAM在Kitti和Euroc数据集中进行了广泛的测试，表明所提出的系统在各种情况下都优于最新方法。
In this paper, we develop a robust, efficient visual SLAM system that utilizes spatial inhibition of low threshold, baseline lines, and closed-loop keyframe features. Using ORB-SLAM2, our methods include stereo matching, frame tracking, local bundle adjustment, and line and point global bundle adjustment. In particular, we contribute re-projection in line with the baseline. Fusing lines in the system consume colossal time, and we reduce the time from distributing points to utilizing spatial suppression of feature points. In addition, low threshold key points can be more effective in dealing with low textures. In order to overcome Tracking keyframe redundant problems, an efficient and robust closed-loop tracking key frame is proposed. The proposed SLAM has been extensively tested in KITTI and EuRoC datasets, demonstrating that the proposed system is superior to state-of-the-art methods in various scenarios.
