Real-Time Monocular Visual SLAM by Combining Points and Lines

Abstract

This paper presents a real-time monocular SLAM algorithm which combines points and line segments. We extend traditional point-based SLAM system with line features which are usually abundant in man-made scenes. The system is more robust and accurate than traditional point-based and direct-based monocular SLAM algorithms. In order to improve the timeliness of multi-feature based SLAM, we propose a novel feature level parallel processing framework and a fast line matching algorithm. For improving the reconstruction accuracy of 3D line segments which is usually affected by unreliable line endpoints, a sample point-based 3D reconstruction algorithm for line segments is proposed. Our system is implemented based on a popular monocular SLAM known as ORB-SLAM and tested on the TUM RGB-D benchmark. The experiment results demonstrate that the proposed system performs better than current state-of-the-art visual SLAM with respect to accuracy.