A visual SLAM method based on point-line fusion in weak-matching scene

Abstract

Visual simultaneous localization and mapping (SLAM) is well-known to be one of the research areas in robotics. There are many challenges in traditional point feature-based approaches, such as insufficient point features, motion jitter, and low localization accuracy in low-texture scenes, which reduce the performance of the algorithms. In this article, we propose an RGB-D SLAM system to handle these situations, which is named Point-Line Fusion (PLF)-SLAM. We utilize both points and line segments throughout the process of our work. Specifically, we present a new line segment extraction method to solve the overlap or branch problem of the line segments, and then a more rigorous screening mechanism is proposed in the line matching section. Instead of minimizing the reprojection error of points, we introduce the reprojection error based on points and lines to get a more accurate tracking pose. In addition, we come up with a solution to handle the jitter frame, which greatly improves tracking success rate and availability of the system. We thoroughly evaluate our system on the Technische Universität München (TUM) RGB-D benchmark and compare it with ORB-SLAM2, presumably the current state-of-the-art solution. The experiments show that our system has better accuracy and robustness compared to the ORB-SLAM2.