TXSLAM: A Monocular Semantic SLAM Tightly Coupled with Planar Text Features

We propose a new monocular semantic simultaneous localization and mapping (SLAM) system that tightly couples planar text features. The system treats text features as a plane with rich texture information and semantic information, and more accurate camera pose estimation can be obtained by tightly coupling the semantic plane. Unlike previous work, it pioneers the use of words contained in the text to represent the semantic information of the plane, which enables the use of simpler and more efficient data association algorithms to match geometric planes. We evaluate our method in public datasets, and the final experimental results prove that our proposed system improves the accuracy of camera pose estimation. Additionally, the system augments the sparse map with semantic plane information, enhancing the applicability of the system in robotics, unmanned driving, augmented reality (AR), and virtual reality (VR).