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As a fundamental task for intelligent robots, visual SLAM has made greatprogress over the past decades. However, robust SLAM under highly weak-texturedenvironments still remains very challenging. In this paper, we propose a novelvisual SLAM system named RWT-SLAM to tackle this problem. We modify LoFTRnetwork which is able to produce dense point matching under low-textured scenesto generate feature descriptors. To integrate the new features into the popularORB-SLAM framework, we develop feature masks to filter out the unreliablefeatures and employ KNN strategy to strengthen the matching robustness. We alsoretrained visual vocabulary upon new descriptors for efficient loop closing.The resulting RWT-SLAM is tested in various public datasets such as TUM andOpenLORIS, as well as our own data. The results shows very promisingperformance under highly weak-textured environments.

RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments