Raisi Zobeir, Zelek John
Electrical Engineering Department, Chabahar Maritime University, Chabahar, Iran.
Vision and Image Processing Laboratory, Systems Design Engineering Department, University of Waterloo, Waterloo, ON, Canada.
Front Robot AI. 2024 Sep 16;11:1424883. doi: 10.3389/frobt.2024.1424883. eCollection 2024.
We live in a visual world where text cues are abundant in urban environments. The premise for our work is for robots to capitalize on these text features for visual place recognition. A new technique is introduced that uses an end-to-end scene text detection and recognition technique to improve robot localization and mapping through Visual Place Recognition (VPR). This technique addresses several challenges such as arbitrary shaped text, illumination variation, and occlusion. The proposed model captures text strings and associated bounding boxes specifically designed for VPR tasks. The primary contribution of this work is the utilization of an end-to-end scene text spotting framework that can effectively capture irregular and occluded text in diverse environments. We conduct experimental evaluations on the Self-Collected TextPlace (SCTP) benchmark dataset, and our approach outperforms state-of-the-art methods in terms of precision and recall, which validates the effectiveness and potential of our proposed approach for VPR.
我们生活在一个视觉世界中，城市环境里文本线索丰富。我们这项工作的前提是让机器人利用这些文本特征进行视觉场所识别。本文介绍了一种新技术，该技术使用端到端场景文本检测与识别技术，通过视觉场所识别（VPR）来改进机器人定位与建图。这项技术解决了诸如任意形状文本、光照变化和遮挡等若干挑战。所提出的模型专门针对VPR任务捕捉文本字符串和相关边界框。这项工作的主要贡献在于利用了一种端到端场景文本检测框架，该框架能够在不同环境中有效捕捉不规则和被遮挡的文本。我们在自收集的TextPlace（SCTP）基准数据集上进行了实验评估，我们的方法在精度和召回率方面优于现有方法，这验证了我们所提出的VPR方法的有效性和潜力。
