Loop Closure Detection for SLAM Based on Siamese Convolutional Neural Network

Author:

Li Xiang,Xu Gang,Zhang Xingyu,Yu Ze,Zhu Zhimin

Abstract

Abstract Loop closure detection is an indispensable part of the SLAM system, which can reduce the cumulative error of mobile robots in positioning and mapping. This paper proposes a supervised learning method, which combines the Siamese network and the VGG16 network in deep learning to handle loop closure detection. The Siamese network method and the traditional method are contrasted in the New College and City Centre data sets to prove the method’s usefulness in this research. The experiment’s findings reveal that the method of this paper ensures precision and recall rate while reducing the time necessary to assess similarities compared to the traditional method. It has a better chance of achieving real-time SLAM.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

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