A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions. Numerous approaches based on deep-learnt image descriptors, sequence matching, domain translation, and probabilistic localization have had success in addressing this challenge, but most rely on the availability of carefully curated representative reference images of the possible places. In this letter, we propose a novel approach, dubbed Bayesian Selective Fusion, for actively selecting and fusing informative reference images to determine the best place match for a given query image. The selective element of our approach avoids the counterproductive fusion of every reference image and enables the dynamic selection of informative reference images in environments with changing visual conditions (such as indoors with flickering lights, outdoors during sunshowers or over the day-night cycle). The probabilistic element of our approach provides a means of fusing multiple reference images that accounts for their varying uncertainty via a novel training-free likelihood function for VPR. On difficult query images from two benchmark datasets, we demonstrate that our approach matches and exceeds the performance of several alternative fusion approaches along with state-of-the-art techniques that are provided with prior (unfair) knowledge of the best reference images. Our approach is well suited for long-term robot autonomy where dynamic visual environments are commonplace since it is training-free, descriptor-agnostic, and complements existing techniques such as sequence matching.

中文翻译：

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通过贝叶斯选择性融合进行视觉位置识别的智能参考管理

视觉位置识别 (VPR) 的一个关键挑战是识别位置，尽管由于时间、季节、天气或照明条件等因素导致视觉外观发生剧烈变化。许多基于深度学习图像描述符、序列匹配、域翻译和概率定位的方法在解决这一挑战方面取得了成功，但大多数方法依赖于精心策划的可能位置的代表性参考图像的可用性。在这封信中，我们提出了一种称为贝叶斯选择性融合的新方法，用于主动选择和融合信息丰富的参考图像，以确定给定查询图像的最佳位置匹配。我们方法的选择性元素避免了每个参考图像的适得其反的融合，并能够在视觉条件不断变化的环境中动态选择信息丰富的参考图像（例如灯光闪烁的室内、阳光明媚的户外或昼夜循环）。我们方法的概率元素提供了一种融合多个参考图像的方法，通过 VPR 的一种新的免训练似然函数来解释它们不同的不确定性。在来自两个基准数据集的困难查询图像上，我们证明我们的方法匹配并超过了几种替代融合方法的性能，以及提供了最佳参考图像的先验（不公平）知识的最先进技术。