In appearance-based localization and mapping, loop closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. As the size of the internal map increases, so does the time required to compare new observations with all stored locations, eventually limiting online processing. This paper presents an online loop closure detection approach for large-scale and long-term operation. The approach is based on a memory management method, which limits the number of locations used for loop closure detection so that the computation time remains under real-time constraints. The idea consists of keeping the most recent and frequently observed locations in a Working Memory (WM) used for loop closure detection, and transferring the others into a Long-Term Memory (LTM). When a match is found between the current location and one stored in WM, associated locations stored in LTM can be updated and remembered for additional loop closure detections. Results demonstrate the approach's adaptability and scalability using ten standard data sets from other appearance-based loop closure approaches, one custom data set using real images taken over a 2 km loop of our university campus, and one custom data set (7 hours) using virtual images from the racing video game ``Need for Speed: Most Wanted''.

翻译：在外观定位与建图技术中，闭环检测是用于判定当前观测是否来自先前访问过的位置或新位置的过程。随着内部地图规模的增大，将新观测与所有存储位置进行比较所需的时间也随之增加，最终会限制在线处理能力。本文提出了一种适用于大规模长期运行的在线闭环检测方法。该方法基于一种内存管理机制，通过限制用于闭环检测的位置数量，使计算时间保持在实时约束范围内。其核心思想是将最近观测到且频繁出现的位置保留在工作记忆（WM）中用于闭环检测，而将其余位置转移至长期记忆（LTM）中。当当前位置与WM中存储的某个位置匹配时，存储在LTM中的关联位置可被更新并记忆，以支持后续的闭环检测。实验使用十组来自其他基于外观的闭环检测方法的标准数据集、一组基于本校校园2公里环线实拍图像的自定义数据集，以及一组使用赛车游戏《极品飞车：最高通缉》虚拟图像的自定义数据集（时长7小时），结果验证了该方法具有适应性和可扩展性。