通过NetVLAD和Faiss实现视觉SLAM中的实时回环检测
Real-Time Loop Closure Detection in Visual SLAM via NetVLAD and Faiss
AI总结：
本文通过NetVLAD和Faiss实现实时回环检测，提升视觉SLAM中定位与建图的精度与鲁棒性。
摘要：
回环检测（LCD）是同时定位与建图（SLAM）的核心组件：它识别已访问的地点，并通过姿态图约束纠正累积的漂移。经典袋-of-words方法如DBoW效率高，但在外观变化和感知别名情况下性能下降。同时，基于深度学习的视觉地点识别（VPR）描述符（如NetVLAD和基于Transformer的模型）具有更强的鲁棒性，但其计算成本常被视为实时SLAM的障碍。本文通过实验证明NetVLAD作为LCD模块的有效性，并在KITTI数据集上将其与DBoW进行比较。我们引入了细粒度的Top-K精度-召回曲线，以更好地反映LCD设置中查询可能有零个或多个有效匹配的情况。通过Faiss加速的最近邻搜索，NetVLAD在实现实时查询速度的同时，提高了准确性并优于DBoW，使其成为SLAM中LCD的实用替代方案。
英文摘要：
Loop closure detection (LCD) is a core component of simultaneous localization and mapping (SLAM): it identifies revisited places and enables pose-graph constraints that correct accumulated drift. Classic bag-of-words approaches such as DBoW are efficient but often degrade under appearance change and perceptual aliasing. In parallel, deep learning-based visual place recognition (VPR) descriptors (e.g., NetVLAD and Transformer-based models) offer stronger robustness, but their computational cost is often viewed as a barrier to real-time SLAM. In this paper, we empirically evaluate NetVLAD as an LCD module and compare it against DBoW on the KITTI dataset. We introduce a Fine-Grained Top-K precision-recall curve that better reflects LCD settings where a query may have zero or multiple valid matches. With Faiss-accelerated nearestneighbor search, NetVLAD achieves real-time query speed while improving accuracy and robustness over DBoW, making it a practical drop-in alternative for LCD in SLAM.
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