  We propose a novel visual SLAM method that integrates text objects tightly by
treating them as semantic features via fully exploring their geometric and
semantic prior. The text object is modeled as a texture-rich planar patch whose
semantic meaning is extracted and updated on the fly for better data
association. With the full exploration of locally planar characteristics and
semantic meaning of text objects, the SLAM system becomes more accurate and
robust even under challenging conditions such as image blurring, large
viewpoint changes, and significant illumination variations (day and night). We
tested our method in various scenes with the ground truth data. The results
show that integrating texture features leads to a more superior SLAM system
that can match images across day and night. The reconstructed semantic 3D text
map could be useful for navigation and scene understanding in robotic and mixed
reality applications. Our project page: https://github.com/SJTU-ViSYS/TextSLAM .
