VDNA-PR: Using General Dataset Representations for Robust Sequential
  Visual Place Recognition
Benjamin Ramtoula Daniele De Martini Matthew Gadd Paul Newman
This paper adapts a general dataset representation technique to produce
robust Visual Place Recognition (VPR) descriptors, crucial to enable real-world
mobile robot localisation. Two parallel lines of work on VPR have shown, on one
side, that general-purpose off-the-shelf feature representations can provide
robustness to domain shifts, and, on the other, that fused information from
sequences of images improves performance. In our recent work on measuring
domain gaps between image datasets, we proposed a Visual Distribution of Neuron
Activations (VDNA) representation to represent datasets of images. This
representation can naturally handle image sequences and provides a general and
granular feature representation derived from a general-purpose model. Moreover,
our representation is based on tracking neuron activation values over the list
of images to represent and is not limited to a particular neural network layer,
therefore having access to high- and low-level concepts. This work shows how
VDNAs can be used for VPR by learning a very lightweight and simple encoder to
generate task-specific descriptors. Our experiments show that our
representation can allow for better robustness than current solutions to
serious domain shifts away from the training data distribution, such as to
indoor environments and aerial imagery.