论文标题
基于线路的大满贯
Line Flow based SLAM
论文作者
论文摘要
我们通过预测和更新代表3D线段的顺序2D投影的线流来提出一个视觉大满贯方法。尽管基于功能的大满贯方法取得了出色的成绩，但它们仍然在包含遮挡，模糊图像和重复性纹理的挑战场景中遇到问题。为了解决这些问题，我们利用线路流以编码沿时间维度相同3D线的线段观测值的连贯性，该线路沿时间维度已在先前的SLAM系统中忽略了。借助此行流表示，可以根据其相应的3D线及其前辈沿时间维度预测新帧中的线段。我们创建，更新，合并和丢弃线路流动。我们使用贝叶斯网络对拟议的基于线路的SLAM（LF-SLAM）进行建模。广泛的实验结果表明，由于线流的利用，提出的LF-SLAM方法可实现最先进的结果。具体而言，LF-SLAM获得了良好的本地化和映射，从而在遮挡，模糊的图像和重复性纹理方面具有挑战性的场景。
We propose a visual SLAM method by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While feature-based SLAM methods have achieved excellent results, they still face problems in challenging scenes containing occlusions, blurred images, and repetitive textures. To address these problems, we leverage a line flow to encode the coherence of line segment observations of the same 3D line along the temporal dimension, which has been neglected in prior SLAM systems. Thanks to this line flow representation, line segments in a new frame can be predicted according to their corresponding 3D lines and their predecessors along the temporal dimension. We create, update, merge, and discard line flows on-the-fly. We model the proposed line flow based SLAM (LF-SLAM) using a Bayesian network. Extensive experimental results demonstrate that the proposed LF-SLAM method achieves state-of-the-art results due to the utilization of line flows. Specifically, LF-SLAM obtains good localization and mapping results in challenging scenes with occlusions, blurred images, and repetitive textures.
