Visual place recognition is a challenging task in the field of computer vision, and autonomous robotics and vehicles, which aims to identify a location or a place from visual inputs. Contemporary methods in visual place recognition employ convolutional neural networks and utilize every region within the image for the place recognition task. However, the presence of dynamic and distracting elements in the image may impact the effectiveness of the place recognition process. Therefore, it is meaningful to focus on task-relevant regions of the image for improved recognition. In this paper, we present PlaceFormer, a novel transformer-based approach for visual place recognition. PlaceFormer employs patch tokens from the transformer to create global image descriptors, which are then used for image retrieval. To re-rank the retrieved images, PlaceFormer merges the patch tokens from the transformer to form multi-scale patches. Utilizing the transformer's self-attention mechanism, it selects patches that correspond to task-relevant areas in an image. These selected patches undergo geometric verification, generating similarity scores across different patch sizes. Subsequently, spatial scores from each patch size are fused to produce a final similarity score. This score is then used to re-rank the images initially retrieved using global image descriptors. Extensive experiments on benchmark datasets demonstrate that PlaceFormer outperforms several state-of-the-art methods in terms of accuracy and computational efficiency, requiring less time and memory.

翻译：视觉位置识别是计算机视觉、自主机器人及自动驾驶领域的一项具有挑战性的任务，其目标是从视觉输入中识别位置或场所。当前的视觉位置识别方法采用卷积神经网络，并利用图像中的每个区域进行位置识别。然而，图像中动态及干扰元素的存在可能会影响位置识别过程的有效性。因此，关注图像中与任务相关的区域以提升识别性能具有重要意义。本文提出PlaceFormer，一种基于Transformer的新型视觉位置识别方法。PlaceFormer利用Transformer的块标记构建全局图像描述符，进而用于图像检索。为了对检索到的图像进行重排序，PlaceFormer融合Transformer的块标记以形成多尺度块。该方法借助Transformer的自注意力机制，选择与图像中任务相关区域对应的块。这些选定块经过几何验证，生成不同块尺寸下的相似度分数。随后，各块尺寸的空间分数被融合以产生最终相似度分数，该分数用于对基于全局图像描述符初步检索到的图像进行重排序。在基准数据集上的大量实验表明，PlaceFormer在准确性和计算效率方面优于多种先进方法，且所需时间和内存更少。