We propose a method of visual SLAM by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While indirect SLAM methods using points and line segments have achieved excellent results, they still face problems in challenging scenarios such as occlusions, image blur, and repetitive textures. To deal with these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment. Thanks to the line flow representation, the corresponding 2D line segment in a new frame can be predicted based on 2D and 3D line segment motions. We create, update, merge, and discard line flows on-the-fly. We model our Line Flow-based SLAM (LF-SLAM) using a Bayesian network. We perform short-term optimization in front-end, and long-term optimization in back-end. The constraints introduced in line flows improve the performance of our LF-SLAM. Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches. Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur, and repetitive textures.

中文翻译：

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基于线流的 SLAM

我们通过预测和更新表示 3D 线段的连续 2D 投影的线流，提出了一种视觉 SLAM 方法。虽然使用点和线段的间接 SLAM 方法取得了优异的结果，但它们在具有挑战性的场景中仍然面临问题，例如遮挡、图像模糊和重复纹理。为了解决这些问题，我们利用线流将空间和时间域中 2D 和 3D 线段的相干性编码为与特定 3D 线段对应的所有 2D 线段的序列。由于线流表示，可以根据 2D 和 3D 线段运动预测新帧中相应的 2D 线段。我们即时创建、更新、合并和丢弃线流。我们使用贝叶斯网络对基于线流的 SLAM (LF-SLAM) 进行建模。我们在前端进行短期优化，在后端进行长期优化。在线流中引入的约束提高了我们的 LF-SLAM 的性能。大量的实验结果表明，我们的方法比最先进的直接和间接 SLAM 方法获得了更好的性能。具体来说，它在具有遮挡、图像模糊和重复纹理的挑战性场景中获得了良好的定位和映射结果。