arXiv 2402.061312024-02-09cs.ROcs.CV

PAS-SLAM：面向平面歧义场景的视觉SLAM系统

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

School of Electronic Information and Electrical Engineering, Dalian University of Technology（大连理工大学电子信息与电气工程学部）；Department of Automation, Tsinghua University（清华大学自动化系）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）

Xinggang Hu, Yanmin Wu, Mingyuan Zhao, Linghao Yang, Xiangkui Zhang, Xiangyang Ji

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AI总结：

提出PAS-SLAM系统，通过融合语义信息的平面处理、集成多源信息的数据关联及多约束因子图优化，有效解决了平面歧义场景下视觉SLAM定位与建图精度差的问题。

摘要：

基于平面特征的视觉SLAM（同时定位与建图）在环境结构感知和增强现实等领域得到了广泛应用。然而，当前研究在平面歧义场景中的准确定位与建图面临挑战，主要原因在于所采用的平面特征和数据关联方法精度较差。本文提出了一种基于平面特征、专为平面歧义场景设计的视觉SLAM系统，涵盖平面处理、数据关联和多约束因子图优化。我们引入了一种将语义信息与平面特征相结合的平面处理策略，提取平面的边缘和顶点，用于平面选择、数据关联和位姿优化等任务。接着，我们提出了一种集成数据关联策略，结合了平面参数、语义信息、投影IoU（交并比）和非参数检验，在平面歧义场景中实现了准确且鲁棒的平面数据关联。最后，我们设计了一组用于相机位姿优化的多约束因子图。在公开数据集上进行的定性和定量实验表明，与最先进的方法相比，我们提出的系统在地图构建和相机定位的准确性与鲁棒性方面均具有竞争力。

英文摘要：

Visual SLAM (Simultaneous Localization and Mapping) based on planar features has found widespread applications in fields such as environmental structure perception and augmented reality. However, current research faces challenges in accurately localizing and mapping in planar ambiguous scenes, primarily due to the poor accuracy of the employed planar features and data association methods. In this paper, we propose a visual SLAM system based on planar features designed for planar ambiguous scenes, encompassing planar processing, data association, and multi-constraint factor graph optimization. We introduce a planar processing strategy that integrates semantic information with planar features, extracting the edges and vertices of planes to be utilized in tasks such as plane selection, data association, and pose optimization. Next, we present an integrated data association strategy that combines plane parameters, semantic information, projection IoU (Intersection over Union), and non-parametric tests, achieving accurate and robust plane data association in planar ambiguous scenes. Finally, we design a set of multi-constraint factor graphs for camera pose optimization. Qualitative and quantitative experiments conducted on publicly available datasets demonstrate that our proposed system competes effectively in both accuracy and robustness in terms of map construction and camera localization compared to state-of-the-art methods.

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