  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.
