我们专注于一个在线2阶段问题，以以下情况进行：考虑一个应分配给大学的系统。在第一轮中，一些学生申请了一些学生，必须计算出第一个（稳定的）匹配$ m_1 $。但是，一些学生可能会决定离开系统（更改计划，去外国大学或不在系统中的某些机构）。然后，在第二轮（在这些删除之后）中，我们将计算第二个（最终）稳定的匹配$ m_2 $。由于不希望更改作业，因此目标是最大程度地减少两个稳定匹配$ m_1 $和$ m_2 $之间的离婚/修改数量。那么，我们应该如何选择$ m_1 $和$ m_2 $？我们表明，有一个{\ it Optival Online}算法可以解决此问题。特别是，由于具有优势属性，我们表明我们可以最佳地计算$ M_1 $，而无需知道会离开系统的学生。我们将结果概括为输入中的其他一些可能的修改（学生，开放位置）。我们还解决了更多阶段的情况，表明在有3个阶段后，就无法实现竞争性（在线）算法。

Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area. This problem has long been acknowledged for its challenging nature in simultaneous localization and mapping (SLAM), wherein the robot needs to correctly associate the incoming sensory data to the database allowing consistent map generation. The significant advances in computer vision achieved over the last 20 years, the increased computational power, and the growing demand for long-term exploration contributed to efficiently performing such a complex task with inexpensive perception sensors. In this article, visual loop closure detection, which formulates a solution based solely on appearance input data, is surveyed. We start by briefly introducing place recognition and SLAM concepts in robotics. Then, we describe a loop closure detection system's structure, covering an extensive collection of topics, including the feature extraction, the environment representation, the decision-making step, and the evaluation process. We conclude by discussing open and new research challenges, particularly concerning the robustness in dynamic environments, the computational complexity, and scalability in long-term operations. The article aims to serve as a tutorial and a position paper for newcomers to visual loop closure detection.