VDNA-PR: 汎用データセット表現を用いた頑健な逐次視覚的位置認識
VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition
画像データセット表現VDNAを軽量エンコーダで変換し、ドメインシフトに強い逐次視覚的位置認識記述子を生成する手法を提案した。
著者: Benjamin Ramtoula, Daniele De Martini, Matthew Gadd, Paul Newman
分類: cs.CV, cs.RO
原文アブストラクト
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.
