Loop Closure Detection (LCD) is an essential component of visual simultaneous localization and mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint variance, and dynamic objects in scenes. This paper introduces an online based on Superpixel Grids (SGs) LCD approach, SGIDN-LCD, to find similarities between scenes via hand-crafted features extracted from SGs. Unlike traditional Bag-of-Words (BoW) models requiring pre-training, we propose an adaptive mechanism to group similar images called $\textbf{\textit{dynamic}}$ $\textbf{\textit{node}}$, which incremental adjusts the database in an online manner, allowing for efficient retrieval of previously viewed images. Experimental results demonstrate the SGIDN-LCD significantly improving LCD precision-recall and efficiency. Moreover, our proposed overall LCD method outperforms state-of-the-art approaches on multiple typical datasets.

翻译：环路闭合检测（LCD）是视觉同步定位与地图构建（SLAM）系统的重要组成部分。它能够识别先前访问过的场景，从而消除长期探索过程中产生的位姿与地图估计漂移。然而，当前基于外观的LCD方法面临计算成本高、视角变化以及场景中存在动态物体等重大挑战。本文提出一种基于超像素网格（SGs）的在线LCD方法SGIDN-LCD，通过从SGs中提取手工特征来寻找场景之间的相似性。区别于需要预训练的经典词袋（BoW）模型，我们提出一种自适应图像分组机制——$\textbf{\textit{动态结点}}$（dynamic node），该机制以在线方式增量调整数据库，从而实现先前视图图像的高效检索。实验结果表明，SGIDN-LCD显著提升了LCD的精度-召回率和效率。此外，我们提出的整体LCD方法在多个典型数据集上的性能优于现有最先进方法。