Superline: A Robust Line Segment Feature for Visual SLAM

Abstract

Along with point features, line features play an important role in achieving robust Simultaneous Localization and Mapping (SLAM) under complex environments. This paper proposes a fast and effective method, namely Superline, to simultaneously detect line segments and generate robust descriptors for matching. The entire model is composed of a convolutional backbone and two task heads, i.e., detection head and description head respectively. A line selecting mechanism and a spatial pyramid Line-of-Interest (LOI) pooling module is specially designed in the description head to aggregate multi-scale information into line feature descriptors. The entire model is implemented end-to-end and can be trained on a dataset with only line annotations and without the need of providing ground truth matching. Comparative experimental results on Wireframe and York Urban datasets as well as applying the Superline features on SLAM applications demonstrate the superior performance of our method.