Abstract
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 text typically appears in indoor spaces, serving to 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 text. Finally, the discriminative text is 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 this https URL.
Abstract (translated)
视觉位置识别（VPR）是长期自主机器人的一项关键能力，使它们能够利用视觉信息来识别之前访问过的地点。然而，现有的方法在室内环境中仍然受限于这些环境中的高度重复结构。我们注意到场景文本通常出现在室内空间中，并且这种文本有助于区分外观相似但实际上不同的地方。这促使我们提出了TextInPlace，这是一种简单而有效的VPR框架，它整合了场景文字检测（STS）以减轻重复的室内环境中视觉感知上的模糊性。 具体来说，TextInPlace采用了一种在局部参数共享网络内的双分支架构。VPR支路使用基于注意力机制的聚合来提取用于粗粒度检索的全局描述符，而STS支路则利用一个桥梁文本检测器来识别和识别人类场景中的文字。最后，它过滤出具有区分性的文本以计算文本相似性并重新排序前K个检索到的图像。 为了弥合现有基于文本的高度重复室内场景数据集与机器人导航中典型的现实情况之间的差距，我们建立了一个名为Maze-with-Text的室内VPR基准数据集。在自定义和公开的数据集中进行的广泛实验表明，TextInPlace优于仅依赖外观信息的现有方法，并且性能更优。 该数据集、代码以及训练模型可在以下网址获取：[链接]（请将方括号中的文字替换为实际链接）。
URL
https://arxiv.org/abs/2503.06501
