【摘要】 回环检测是同步定位与地图构建系统的组成模块,目前大多数回环检测算法从数据帧提取特征描述子,通过描述子之间的欧氏距离搜索回环,未对提取的特征描述子进行特征增强.针对上述问题,文中提出基于度量学习的回环检测描述子提升算法.设计轻量级算法模块,对生成的描述子进行特征空间变换,增强描述子的区分能力,有效提升回环检测性能.通过位姿和描述子结合的方式成组制作三元组数据集,解决标签模糊的问题.提出扩充数据集的思路,解决回环样本显著不足的问题.基于三元组损失函数改造损失函数,适配回环检测场景,训练用于特征空间变换的神经网络模块.在KITTI、NCLT数据集上的测试表明,文中算法具有较强的泛化能力.更多还原

【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.更多还原