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 .

本文提出一种新颖的视觉SLAM方法，通过充分探索文本对象的几何和语义先验将其作为语义特征紧密集成，将文本对象建模为纹理丰富的平面贴片，其语义意义在飞行中提取和更新以进行更好的数据关联。通过局部平面特征和文本对象的语义意义的充分探索，SLAM系统即使在挑战性条件（如图像模糊、大视角变化和昼夜明显变化）下也变得更加精确和稳健。我们在各种场景中测试了我们的方法，并使用真实数据进行比较。结果表明，集成纹理特征导致更优秀的SLAM系统，可以跨越昼夜匹配图像。重建的语义3D文本地图在机器人和混合现实应用中具有导航和场景理解的价值。

TextSLAM: 带语义平面文本特征的视觉SLAM