This paper presents a self-supervised learning network called SP-Flow to generate keypoints in real-time for SLAM systems. Optical flows are employed to match the keypoints between two successive frames in the training process of SP-Flow. This approach enables the network to use datasets without manual annotations. To show the efficacy of our SP-Flow, we built an SP-Flow SLAM system by replacing ORB with SP-Flow in the ORB-SLAM2 system. The experimental results demonstrate that our SLAM system is able to achieve real-time performance and high accuracy with stereo or RGB-D images as inputs.

中文翻译：

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SP-Flow：实时SLAM的自监督光流对应点预测

本文提出了一种称为SP-Flow的自我监督学习网络，可为SLAM系统实时生成关键点。在SP-Flow的训练过程中，采用光流来匹配两个连续帧之间的关键点。这种方法使网络可以使用没有手动注释的数据集。为了显示SP-Flow的功效，我们通过在ORB-SLAM2系统中用SP-Flow替换ORB来构建SP-Flow SLAM系统。实验结果表明，我们的SLAM系统能够以立体声或RGB-D图像作为输入来实现实时性能和高精度。