用语言驱动的语义分割构建视觉定位描述符，无需训练也能抗场景变化。
Context-Based Visual-Language Place Recognition
- 通过零样本语言模型提取像素级语义嵌入，生成语义描述符
- 在真实数据集上优于传统特征和现成CNN描述符
- 无需额外训练，适合快速部署于动态环境
基于视觉的机器人定位与建图中，视觉位置识别（VPR）至关重要。现有方法多依赖低层视觉特征，但在场景外观变化时表现不佳。虽然端到端学习方法可缓解此问题，但仍需大量标注数据且在剧烈变化下失效。为此，本文提出一种新VPR方法，利用零样本、语言驱动的语义分割模型提取像素级嵌入，构建语义图像描述符，实现对场景变化的鲁棒性。该方法无需额外训练，在真实世界公开数据集上的实验表明，其性能优于非学习型图像表示和现成的卷积神经网络（CNN）描述符。代码已开源。
原文摘要 · Abstract (English)
In vision-based robot localization and SLAM, Visual Place Recognition (VPR) is essential. This paper addresses the problem of VPR, which involves accurately recognizing the location corresponding to a given query image. A popular approach to vision-based place recognition relies on low-level visual features. Despite significant progress in recent years, place recognition based on low-level visual features is challenging when there are changes in scene appearance. To address this, end-to-end training approaches have been proposed to overcome the limitations of hand-crafted features. However, these approaches still fail under drastic changes and require large amounts of labeled data to train models, presenting a significant limitation. Methods that leverage high-level semantic information, such as objects or categories, have been proposed to handle variations in appearance. In this paper, we introduce a novel VPR approach that remains robust to scene changes and does not require additional training. Our method constructs semantic image descriptors by extracting pixel-level embeddings using a zero-shot, language-driven semantic segmentation model. We validate our approach in challenging place recognition scenarios using real-world public dataset. The experiments demonstrate that our method outperforms non-learned image representation techniques and off-the-shelf convolutional neural network (CNN) descriptors. Our code is available at https: //github.com/woo-soojin/context-based-vlpr.
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