Visual SLAM is a key technology for many autonomous systems. However, tracking loss can lead to the creation of disjoint submaps in multimap SLAM systems like ORB-SLAM3. Because of that, these systems employ submap merging strategies. As we show, these strategies are not always successful. In this paper, we investigate the impact of using modern VPR approaches for submap merging in visual SLAM. We argue that classical evaluation metrics are not sufficient to estimate the impact of a modern VPR component on the overall system. We show that naively replacing the VPR component does not leverage its full potential without requiring substantial interference in the original system. Because of that, we present a post-processing pipeline along with a set of metrics that allow us to estimate the impact of modern VPR components. We evaluate our approach on the NCLT and Newer College datasets using ORB-SLAM3 with NetVLAD and HDC-DELF as VPR components. Additionally, we present a simple approach for combining VPR with temporal consistency for map merging. We show that the map merging performance of ORB-SLAM3 can be improved. Building on these results, researchers in VPR can assess the potential of their approaches for SLAM systems.

翻译：视觉SLAM是众多自主系统的关键技术。然而，在ORB-SLAM3等多地图SLAM系统中，跟踪丢失可能导致生成互不连通的子地图。因此，这些系统采用了子地图融合策略。如我们所示，这些策略并非总能成功。本文研究了在视觉SLAM中使用现代视觉位置重识别方法进行子地图融合的影响。我们认为经典评估指标不足以衡量现代VPR组件对整个系统的影响。研究表明，若不对原系统进行重大干预，简单替换VPR组件无法充分发挥其潜力。为此，我们提出了一套后处理流程及相应评估指标，用以量化现代VPR组件的实际影响。我们在NCLT和Newer College数据集上使用ORB-SLAM3系统，以NetVLAD和HDC-DELF作为VPR组件进行评估验证。此外，我们提出了一种结合时序一致性与VPR的简易地图融合方法。实验证明该方法能有效提升ORB-SLAM3的地图融合性能。基于这些成果，VPR领域的研究者可据此评估其方法在SLAM系统中的适用潜力。