TextSLAM: Visual SLAM with Semantic Planar Text Features
This work addresses the need for more reliable SLAM systems in robotics and mixed reality applications, though it is incremental as it builds on existing SLAM methods by adding text features.
The authors tackled the problem of improving visual SLAM accuracy and robustness under challenging conditions by integrating text objects as semantic planar features, resulting in a system that can match images across day and night with superior performance.
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 .