PAS-SLAM: A Visual SLAM System for Planar-Ambiguous Scenes

• Published in: IEEE transactions on circuits and systems for video technology 2024-11, p.1-1
• Main Authors: Hu, Xinggang, Wu, Yanmin, Zhao, Mingyuan, Yang, Linghao, Zhang, Xiangkui, Ji, Xiangyang
• Format: Article
• Language: English
• Subjects: Accuracy; Cameras; Data Association; Electronic mail; Feature extraction; Location awareness; Multi-Constraint Factor Graph; Optimization; Planar Ambiguous Scenes; Planar Analysis and Processing; Planar Features; Pose estimation; Semantics; Simultaneous localization and mapping; Visual SLAM; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2024.3491506

Visual SLAM (Simultaneous Localization and Mapping) systems based on planar features have been widely applied in fields such as environmental structure perception and augmented reality (AR). However, current research still faces challenges in accurate localization and map construction in planar ambiguous scenes, primarily due to the insufficient accuracy of the planar features and data association methods employed. In this paper, we propose a visual SLAM system based on planar features designed for ambiguous planar scenes, including planar analysis and processing, data association, and multi-constraint factor graph optimization. Initially, we introduce a planar analysis and processing strategy that integrates semantic information to analyze the structure of planes and further refine the selection of planes, providing accurate planar information for subsequent association and optimization processes. Then, we integrate various planar data to propose a multimodal fusion data association strategy, achieving accurate and robust planar data association in ambiguous planar scenes. Finally, based on accurate and rich planar information along with related constraints, we design a set of multi-constraint factor graphs for camera pose optimization. Public datasets and real-world experiments demonstrate that, compared to state-of-the-art related research, our proposed system shows significant competitive advantages in terms of accuracy and robustness for both map construction and camera localization. Regarding quantifiable localization accuracy, our system achieves an average improvement in Absolute Trajectory Error (ATE) of approximately 57% in planar ambiguous scenes and about 25% in non-planar ambiguous scenes. Additionally, the system exhibits great application potential in fields such as augmented reality.