Visual place recognition from end-to-end semantic scene text features

Area of Science:

• Computer Vision
• Robotics
• Artificial Intelligence

Background:

• Urban environments provide abundant visual text cues.
• Robots can leverage these text features for enhanced place recognition.
• Current Visual Place Recognition (VPR) methods face challenges with text variations.

Purpose of the Study:

• To introduce a novel technique for robots to utilize urban text for Visual Place Recognition (VPR).
• To improve robot localization and mapping capabilities by integrating scene text detection and recognition.

Main Methods:

• Developed an end-to-end scene text detection and recognition technique tailored for VPR.
• The model captures text strings and bounding boxes, addressing challenges like arbitrary shapes, illumination variations, and occlusion.
• Utilized an end-to-end scene text spotting framework for robust text capture in diverse conditions.

Main Results:

• Experimental evaluations were conducted on the Self-Collected TextPlace (SCTP) benchmark dataset.
• The proposed approach demonstrated superior performance compared to state-of-the-art methods.
• Achieved significant improvements in precision and recall for VPR tasks.

Conclusions:

• The proposed end-to-end scene text spotting framework is effective for VPR.
• This approach shows significant potential for improving robot localization and mapping in complex urban environments.
• The method successfully handles challenges posed by irregular and occluded text.