School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 240302, China.
Sensors (Basel). 2021 Jun 30;21(13):4499. doi: 10.3390/s21134499.
Loop Closure Detection (LCD) is an important technique to improve the accuracy of Simultaneous Localization and Mapping (SLAM). In this paper, we propose an LCD algorithm based on binary classification for feature matching between similar images with deep learning, which greatly improves the accuracy of LCD algorithm. Meanwhile, a novel lightweight convolutional neural network (CNN) is proposed and applied to the target detection task of key frames. On this basis, the key frames are binary classified according to their labels. Finally, similar frames are input into the improved lightweight feature matching network based on Transformer to judge whether the current position is loop closure. The experimental results show that, compared with the traditional method, LFM-LCD has higher accuracy and recall rate in the LCD task of indoor SLAM while ensuring the number of parameters and calculation amount. The research in this paper provides a new direction for LCD of robotic SLAM, which will be further improved with the development of deep learning.
环路检测（Loop Closure Detection，LCD）是提高同时定位与建图（Simultaneous Localization and Mapping，SLAM）精度的重要技术。本文提出了一种基于深度学习的二进制分类特征匹配的环路检测算法，大大提高了环路检测算法的准确性。同时，提出了一种新颖的轻量级卷积神经网络（Convolutional Neural Network，CNN），并将其应用于关键帧的目标检测任务。在此基础上，根据标签对关键帧进行二进制分类。最后，将相似帧输入到基于 Transformer 的改进的轻量级特征匹配网络中，判断当前位置是否发生环路闭合。实验结果表明，与传统方法相比，LFM-LCD 在保证参数数量和计算量的同时，在室内 SLAM 的 LCD 任务中具有更高的准确性和召回率。本文的研究为机器人 SLAM 的 LCD 提供了新的方向，随着深度学习的发展，它将得到进一步的改进。
