  As a fundamental task for intelligent robots, visual SLAM has made great
progress over the past decades. However, robust SLAM under highly weak-textured
environments still remains very challenging. In this paper, we propose a novel
visual SLAM system named RWT-SLAM to tackle this problem. We modify LoFTR
network which is able to produce dense point matching under low-textured scenes
to generate feature descriptors. To integrate the new features into the popular
ORB-SLAM framework, we develop feature masks to filter out the unreliable
features and employ KNN strategy to strengthen the matching robustness. We also
retrained visual vocabulary upon new descriptors for efficient loop closing.
The resulting RWT-SLAM is tested in various public datasets such as TUM and
OpenLORIS, as well as our own data. The results shows very promising
performance under highly weak-textured environments.
