A visual odometry with planar markers and feature point cues

In recent years, visual SLAM technology has matured significantly. SLAM systems usually depend on natural feature points to acquire precise motion information, however, these methods frequently encounter tracking failures in scenes characterized by weak or repetitive textures. This paper proposes a Marker-based visual SLAM system by fusing Marker-based and feature point-based Cues. We use Marker-point cues in tracking and extract Marker-plane cues from geometric lines in mapping. ORB feature points, marker features, and plane features collaboratively contribute to local map optimization. Our method has been compared with the latest SLAM systems on both the public dataset and our dataset. The results demonstrate that our method improves accuracy in scenes with weak textures, and enhances robustness in challenging texture-less regions.