Abstract：Loop detection is an important part of simultaneous localization and mapping (SLAM). In most of the loop detection algorithms, feature descriptors are extracted from data frames, and loops are searched through the Euclidean distance between the descriptors. However, feature enhancement is not conducted on the extracted feature descriptors. In this paper, an algorithm of feature descriptor enhancement for loop detection based on metric learning is proposed. A lightweight algorithm module is designed to transform the feature space of the descriptors to enhance the distinguishing ability of the descriptors and improve the loop detection performance effectively. Pose and descriptors are combined to establish a triple dataset and thus the problem of fuzzy labels is solved. An idea of expanding the dataset is proposed to solve the problem of significantly insufficient loop samples. Based on triplet loss, the proposed loss function is adapted to the loop detection scene, and it is utilized to train a neural network module for feature space transformation. Experiments on KITTI and NCLT datasets show that the generalization ability of the proposed algorithm is strong.
韩彬, 罗伦, 刘雄伟, 沈会良. 基于度量学习的回环检测描述子提升算法[J]. 模式识别与人工智能, 2022, 35(1): 51-61.
HAN Bin, LUO Lun, LIU Xiongwei, SHEN Huiliang. Feature Descriptor Enhancement for Loop Detection Based on Metric Learning. Pattern Recognition and Artificial Intelligence, 2022, 35(1): 51-61.
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2022, Vol. 35 
