Loop closure detection is the process involved when trying to find a match between the current and a previously visited locations in SLAM. Over time, the amount of time required to process new observations increases with the size of the internal map, which may influence real-time processing. In this paper, we present a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on a memory management method that keeps computation time for each new observation under a fixed limit. Results demonstrate the approach's adaptability and scalability using four standard data sets.

翻译：回环检测是SLAM中识别当前位置与历史访问位置匹配关系的过程。随着内部地图规模的增长，处理新观测数据所需的时间会持续增加，这可能影响实时处理性能。本文提出一种适用于大规模长期SLAM的实时回环检测新方法。该方法基于内存管理机制，确保每个新观测数据的计算时间维持在固定阈值内。通过在四个标准数据集上的实验，结果验证了该方法具有优异的适应性和可扩展性。