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.

本研究介绍了一种基于超像素网格的在线闭环检测方法，提出了一种动态节点适应机制，并通过实验结果验证了该方法在提高闭环检测精度和效率方面的有效性，优于当前其他的检测方法。

基于外观的环路闭合检测算法：使用超像素网格和增量动态节点的 SGIDN-LCD

SGIDN-LCD: An Appearance-based Loop Closure Detection Algorithm using  Superpixel Grids and Incremental Dynamic Nodes

Link prediction is a popular research area with important applications in a
variety of disciplines, including biology, social science, security, and
medicine. The fundamental requirement of link prediction is the accurate and
effective prediction of new links in networks. While there are many different
methods proposed for link prediction, we argue that the practical performance
potential of these methods is often unknown because of challenges in the
evaluation of link prediction, which impact the reliability and reproducibility
of results. We describe these challenges, provide theoretical proofs and
empirical examples demonstrating how current methods lead to questionable
conclusions, show how the fallacy of these conclusions is illuminated by
methods we propose, and develop recommendations for consistent, standard, and
applicable evaluation metrics. We also recommend the use of precision-recall
threshold curves and associated areas in lieu of receiver operating
characteristic curves due to complications that arise from extreme imbalance in
the link prediction classification problem.

本文研究了链接预测领域中评估方法的挑战，提出了新的方法以替代目前方法，提供准确可靠的预测结果，并建议使用精确度 - 召回率曲线和相关面积来解决链接预测分类问题中存在的极端不平衡挑战。