Recent work by Suenderhauf et al. [1] demonstrated improved visual place
recognition using proposal regions coupled with features from convolutional
neural networks (CNN) to match landmarks between views. In this work we extend
the approach by introducing descriptors built from landmark features which also
encode the spatial distribution of the landmarks within a view. Matching
descriptors then enforces consistency of the relative positions of landmarks
between views. This has a significant impact on performance. For example, in
experiments on 10 image-pair datasets, each consisting of 200 urban locations
with significant differences in viewing positions and conditions, we recorded
average precision of around 70% (at 100% recall), compared with 58% obtained
using whole image CNN features and 50% for the method in [1].

本研究中，我们建立了从地标特征中提取的描述符，该描述符还编码了视图中地标的空间分布，匹配描述符然后强制实施地标在视图之间的相对位置的一致性，这对性能有显着影响。在 10 个图像对数据集上的实验中，每个数据集都由 200 个城市地点组成，观察位置和条件有显着差异，我们记 录了大约 70％ 的平均精度（在 100％ 召回率下），而使用整个图像 CNN 特征获得了 58％，[1] 方法获得了 50％。