Visual Place Recognition is a challenging task for robotics and autonomous
systems, which must deal with the twin problems of appearance and viewpoint
change in an always changing world. This paper introduces Patch-NetVLAD, which
provides a novel formulation for combining the advantages of both local and
global descriptor methods by deriving patch-level features from NetVLAD
residuals. Unlike the fixed spatial neighborhood regime of existing local
keypoint features, our method enables aggregation and matching of deep-learned
local features defined over the feature-space grid. We further introduce a
multi-scale fusion of patch features that have complementary scales (i.e. patch
sizes) via an integral feature space and show that the fused features are
highly invariant to both condition (season, structure, and illumination) and
viewpoint (translation and rotation) changes. Patch-NetVLAD outperforms both
global and local feature descriptor-based methods with comparable compute,
achieving state-of-the-art visual place recognition results on a range of
challenging real-world datasets, including winning the Facebook Mapillary
Visual Place Recognition Challenge at ECCV2020. It is also adaptable to user
requirements, with a speed-optimised version operating over an order of
magnitude faster than the state-of-the-art. By combining superior performance
with improved computational efficiency in a configurable framework,
Patch-NetVLAD is well suited to enhance both stand-alone place recognition
capabilities and the overall performance of SLAM systems.

本文介绍了一种名为 Patch-NetVLAD 的方法，它提供了一种将局部和全局描述符方法的优点相结合的新方法，通过从 NetVLAD 剩余变量中导出局部级别的特征，我们的方法使深度学习的局部特征定义在特征空间网格上，能够聚合和匹配这些特征，并通过积分特征空间进行互补尺度（即块大小）的块特征的多尺度融合，显示出在其状态 - of-the-art 视觉地点识别结果上具有高度不变性，超过了基于全局和局部特征描述符的方法，同时还具有可配置框架中的改进计算效率，适合于增强独立场所识别能力和 SLAM 系统的整体性能。