UL-SLAM: A Universal Monocular Line-Based SLAM Via Unifying Structural and Non-Structural Constraints

Leveraging structural line features to complement sparse point features has been studied in recent years. However, this approach relies on a Manhattan world assumption and does not incorporate non-structural lines due to the triangulation degeneracy problem and tracking process instability. To address these problems, we propose a general line-based SLAM system that combines points, structural and non-structural lines. First, an efficient line matching algorithm for multi-scale is designed to obtain more accurate matching pairs through a divide-and-conquer approach. In addition, a novel line triangulation strategy utilizing spatial-temporal consistency and degeneracy identification is proposed to improve the quality of line generation in a sliding window. Finally, universal structural constraints based on measurement of the vanishing directions are implemented to complement the information missing from the Plücker line projection in local mapping optimization. Extensive experiments are conducted on the public EuRoC and TUM datasets as well as a self-collected dataset, and the results show that UL-SLAM achieves cutting-edge performance among recent state-of-the-art methods in both accuracy and speed. Ablation experiments also demonstrate that the integration of different line features can improve the robustness and accuracy of a visual SLAM system in challenging scenarios with low texture and weak illumination. Our implementation of the UL-SLAM will be open-sourced to benefit the community (https://github.com/jhch1995/UL-SLAM). Note to Practitioners—This article was motivated by the challenges of visual localization problems in human-made indoor scenes. Visual localization has been widely used in various robotic fields such as self-driving vehicles, augmented reality (AR), and virtual reality (VR). In real-world scenes, the localization accuracy will be significantly decreased because of the sparse and uncertain visual features in low-texture or weak illumination environments, which reduces the robustness of robot tracking. To address this problem, this article proposes a novel universal line-based SLAM system (UL-SLAM) that unifies structural and non-structural constraints within a general framework unrestricted by the strong global Manhattan world assumption. UL-SLAM can not only improve the accuracy of pose estimation due to the proposed methods for the line features but also achieve real-time performance. In addition, for 3D mapping construction, UL-SLAM can also enrich the geometric structure information of the indoor scenes. Extensive experiments are conducted on various indoor datasets for autonomous robots, and the results demonstrate the efficiency, accuracy, and robustness of the proposed system in different complex scenarios.