Research on Loop Closure Detection Method Based on ResNet

Abstract

SLAM technology is an important basis for autonomous navigation of robots, and loop closure detection is an important part of SLAM technology. Its task is to identify whether the robot has moved to its current position, which has a good role in drawing map. In this paper, a feature extraction model is obtained by training the custom data set on the ResNet50 residual network, which can extract features with better robustness. Then, the twin-network and terny-loss function are introduced to improve the network performance through weak supervised training. Finally, the cosine similarity method is used to calculate the similarity <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</sup> . If the similarity exceeds the threshold, it is considered that there is a loop. After the comparison experiment between the proposed method and the traditional method on the open data set, it is found that the ResNet50 network model improves the feature extraction ability and the loop detection accuracy, which proves the feasibility and application value of this paper.