TextInPlace：基于场景文本检测与验证的重复结构室内视觉位置识别
TextInPlace: Indoor Visual Place Recognition in Repetitive Structures with Scene Text Spotting and Verification
AI总结：
针对室内重复结构导致的视觉位置识别困难，提出TextInPlace框架，通过集成场景文本检测与双分支架构提取文本特征进行重排序，并构建Maze-with-Text基准，显著提升了识别性能。
摘要：
视觉位置识别（VPR）是长期自主机器人的一项关键能力，使其能够利用视觉信息识别先前访问过的位置。然而，由于此类环境中固有的高度重复结构，现有方法在室内环境中的表现仍然受限。我们观察到场景文本在室内空间中频繁出现，能够帮助区分视觉上相似但实际不同的地点。这启发我们提出TextInPlace，一个简单而有效的VPR框架，该框架集成了场景文本检测（STS）以缓解重复室内环境中的视觉感知歧义。具体而言，TextInPlace在局部参数共享网络内采用双分支架构。VPR分支采用基于注意力的聚合来提取全局描述符以进行粗粒度检索，而STS分支利用桥接文本检测器来检测和识别场景文本。最后，过滤出具有区分性的文本以计算文本相似度，并对检索到的Top-K图像进行重新排序。为了弥合当前基于文本的重复室内场景数据集与机器人导航中遇到的典型场景之间的差距，我们建立了一个室内VPR基准数据集，称为Maze-with-Text。在自定义和公开数据集上的大量实验表明，TextInPlace相比仅依赖外观信息的现有方法取得了更优的性能。数据集、代码和训练模型已在 https://github.com/HqiTao/TextInPlace 公开。
英文摘要：
Visual Place Recognition (VPR) is a crucial capability for long-term autonomous robots, enabling them to identify previously visited locations using visual information. However, existing methods remain limited in indoor settings due to the highly repetitive structures inherent in such environments. We observe that scene texts frequently appear in indoor spaces and can help distinguish visually similar but different places. This inspires us to propose TextInPlace, a simple yet effective VPR framework that integrates Scene Text Spotting (STS) to mitigate visual perceptual ambiguity in repetitive indoor environments. Specifically, TextInPlace adopts a dual-branch architecture within a local parameter sharing network. The VPR branch employs attention-based aggregation to extract global descriptors for coarse-grained retrieval, while the STS branch utilizes a bridging text spotter to detect and recognize scene texts. Finally, the discriminative texts are filtered to compute text similarity and re-rank the top-K retrieved images. To bridge the gap between current text-based repetitive indoor scene datasets and the typical scenarios encountered in robot navigation, we establish an indoor VPR benchmark dataset, called Maze-with-Text. Extensive experiments on both custom and public datasets demonstrate that TextInPlace achieves superior performance over existing methods that rely solely on appearance information. The dataset, code, and trained models are publicly available at https://github.com/HqiTao/TextInPlace.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
